computer science phd courses

CS PhD Course Guidelines

The following program guidelines (a.k.a model pogram) serve as a starting point for a discussion with the faculty about areas of interest.   This description of the Computer Science PhD course guidelines augments the school-wide  PhD course requirements .   Students should make themselves familiar with both.

Course Guidelines for Ph.D. Students in Computer Science

We expect students to obtain broad knowledge of computer science by taking graduate level courses in a variety of sub-areas in computer science, such as systems, networking, databases, algorithms, complexity, hardware, human-computer interaction, graphics, or programming languages.

Within our school, CS courses are roughly organized according to sub-area by their middle digit, so we expect students to take courses in a minimum of three distinct sub-areas, one of which should be theory (denoted by the middle digit of 2, or CS 231). Theory is specifically required as we expect all students to obtain some background in the mathematical foundations that underlie computer science. The intention is not only to give breadth to students, but to ensure cross-fertilization across different sub-disciplines in Computer Science.

Just as we expect all students obtaining a Ph.D. to have experience with the theoretical foundations of computer science, we expect all students to have some knowledge of how to build large software or hardware systems , on the order of thousands of lines of code, or the equivalent complexity in hardware. That experience may be evidenced by coursework or by a project submitted to the CHD for examination. In almost all cases a course numbered CS 26x or CS 24x will satisfy the requirement (exceptions will be noted in the course description on my.harvard). Students may also petition to use CS 161 for this requirement.   For projects in other courses, research projects, or projects done in internships the student is expected to write a note explaining the project, include a link to any relevant artifacts or outcomes, describe the student's individual contribution, and where appropriate obtain a note from their advisor, their class instructor, or their supervisors confirming their contributions.  The project must include learning about systems concepts, and not just writing many lines of code.   Students hoping to invoke the non-CS24x/26x/161 option must consult with  Prof. Mickens ,  Prof, Kung,  or  Prof. Idreos  well in advance of submitting their Program Plan to the CHD.  

Computer science is an applied science, with connections to many fields. Learning about and connecting computer science to other fields is a key part of an advanced education in computer science. These connections may introduce relevant background, or they may provide an outlet for developing new applications.

For example, mathematics courses may be appropriate for someone working in theory, linguistics courses may be appropriate for someone working in computational linguistics, economics courses may be appropriate for those working in algorithmic economics, electrical engineering courses may be appropriate for those working in circuit design, and design courses may be appropriate for someone working in user interfaces.

Requirements

The Graduate School of Arts & Sciences (GSAS) requires all Ph.D. students to complete 16 half-courses (“courses”, i.e., for 4 units of credit) to complete their degree. Of those 16 courses, a Ph.D. in Computer Science requires 10 letter-graded courses. (The remaining 6 courses are often 300-level research courses or other undergraduate or graduate coursework beyond the 10 required courses.)

The requirements for the 10 letter-graded courses are as follows:

• Of the 7 technical courses, at least 3 must be 200-level Computer Science courses, with 3 different middle digits (from the set 2,3,4,5,6,7,8), and with one of these three courses either having a middle digit of 2 or being CS 231 (i.e., a “theory” course).   Note that CS courses with a middle digit of 0 are valid technical courses, but do not contribute to the breadth requirement.
• At least 5 of the 8 disciplinary courses must be SEAS or SEAS-equivalent 200-level courses. A “SEAS equivalent” course is a course taught by a SEAS faculty member in another FAS department. 
• For any MIT course taken, the student must provide justification why the MIT course is necessary (i.e. SEAS does not offer the topic, the SEAS course has not been offered in recent years, etc.). MIT courses do not count as part of the 5 200-level SEAS/SEAS-equivalent courses. 
• 2 of the 10 courses must constitute an external minor (referred to as "breadth" courses in the SEAS “ Policies of the Committee on Higher Degrees [CHD] ”) in an area outside of computer science. These courses should be clearly related; generally, this will mean the two courses are in the same discipline, although this is not mandatory. These courses must be distinct from the 8 disciplinary courses referenced above.
• Students must demonstrate practical competence by building a large software or hardware system during the course of their graduate studies. This requirement will generally be met through a class project, but it can also be met through work done in the course of a summer internship, or in the course of research.
• In particular, for Computer Science graduate degrees, Applied Computation courses may be counted as 100-level courses, not 200-level courses.
• Up to 2 of the 10 courses can be 299r courses, but only 1 of the up to 2 allowed 299r courses can count toward the 8 disciplinary courses. 299r courses do not count toward the 5 200-level SEAS/SEAS-equivalent courses. If two 299r’s are taken, they can be with the same faculty but the topics must be sufficiently different.
• A maximum of 3 graduate-level transfer classes are allowed to count towards the 10 course requirement.
• All CS Ph.D. program plans must adhere to the SEAS-wide Ph.D. requirements, which are stated in the SEAS Policies of the Committee on Higher Degrees (CHD) . These SEAS-wide requirements are included in the items listed above, though students are encouraged to read the CHD document if there are questions, as the CHD document provides further explanation/detail on several of the items above.
• All program plans must be approved by the CHD. Exceptions to any of these requirements require a detailed written explanation of the reasoning for the exception from the student and the student’s research advisor. Exceptions can only be approved by the CHD, and generally exceptions will only be given for unusual circumstances specific to the student’s research program.

Requirement Notes

• Courses below the 100-level are not suitable for graduate credit.
• For students who were required to take it, CS 2091/2092 (formerly CS 290a/b or 290hfa/hfb may be included as one of the 10 courses but it does not count toward the 200-level CS or SEAS/SEAS-equivalent course requirements nor toward the SM en route to the PhD.

Your program plan  must always comply  with both our school's General Requirements, in addition to complying with the specific requirements for Computer Science. All program plans must be approved by the Committee on Higher Degrees [CHD]. Exceptions to the requirements can only be approved by the CHD, and generally will only be given for unusual circumstances specific to the student’s research program

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Academics   /   Graduate PhD in Computer Science

The doctor of philosophy in computer science program at Northwestern University primarily prepares students to become expert independent researchers. PhD students conduct original transformational research in extant and emerging computer science topics. Students work alongside top researchers to advance the core CS fields from Theory to AI and Systems and Networking . In addition, PhD students have the opportunity to collaborate with CS+X faculty who are jointly appointed between CS and disciplines including business, law, economics, journalism, and medicine.

Joining a Track

Doctor of philosophy in computer science students follow the course requirements, qualifying exam structure, and thesis process specific to one of five tracks :

• Artificial Intelligence and Machine Learning
• Computer Engineering

Within each track, students explore many areas of interest, including programming languages , security and privacy and human-computer interaction .

Learn more about computer science research areas

Curriculum and Requirements

The focus of the CS PhD program is learning how to do research by doing research, and students are expected to spend at least 50% of their time on research. Students complete ten graduate curriculum requirements (including COMP_SCI 496: Introduction to Graduate Studies in Computer Science ), and additional course selection is tailored based on individual experience, research track, and interests. Students must also successfully complete a qualifying exam to be admitted to candidacy.

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Download a PDF program guide about your program of interest and get in contact with our graduate admissions staff.

Request info about the PhD degree

Opportunities for PhD Students

Cognitive science certificate.

Computer science PhD students may earn a specialization in cognitive science by taking six cognitive science courses. In addition to broadening a student’s area of study and improving their resume, students attend cognitive science events and lectures, they can receive conference travel support, and they are exposed to cross-disciplinary exchanges.

The Crown Family Graduate Internship Program

PhD candidates may elect to participate in the Crown Family Graduate Internship Program. This opportunity allows the doctoral candidate to gain practical experience in industry or in national research laboratories in areas closely related to their research.

Management for Scientists and Engineers Certificate Program

The certificate program — jointly offered by The Graduate School and Kellogg School of Management — provides post-candidacy doctoral students with a basic understanding of strategy, finance, risk and uncertainty, marketing, accounting and leadership. Students are introduced to business concepts and specific frameworks for effective management relevant to both for-profit and nonprofit sectors.

Career Paths

Recent graduates of the computer science PhD program are pursuing careers in industry & research labs, academia, and startups.

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What Students Are Saying

"One great benefit of Northwestern is the collaborative effort of the CS department that enabled me to work on projects involving multiple faculty, each with their own diverse set of expertise.

Northwestern maintains a great balance: you will work on leading research at a top-tier institution, and you won't get lost in the mix."

— Brian Suchy, PhD Candidate, Computer Systems

What Alumni Are Saying

"In the early stage of my PhD program, I took several courses from the Department of Economics and the Kellogg School of Management and, later, I started collaborating with researchers in those areas. The experience taught me how to have an open mind to embrace and work with people with different backgrounds."

— Yiding Feng (PhD '21), postdoctoral researcher, Microsoft Research Lab – New England

Read an alumni profile of Yiding Feng

"My work at IBM Research involves bringing together symbolic and deep learning techniques to solve problems in interpretable, effective ways, which means I must draw upon the research I did at Northwestern quite frequently."

— Maxwell Crouse (PhD '21), AI Research Scientist, IBM Research

Read an alumni profile of Maxwell Crouse

The theory group here is very warm and close-knit. Starting a PhD is daunting, and it is comforting to have a community I can lean on.

— Vaidehi Srinivas, PhD Candidate, CS Theory

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Phd program, find your passion for research.

Duke Computer Science gives incoming students an opportunity to investigate a range of topics, research problems, and research groups before committing to an advisor in the first year. Funding from the department and Duke makes it possible to attend group meetings, seminars, classes and colloquia. Students may work on multiple problems simultaneously while finding the topic that will motivate them through their first project. Sharing this time of learning and investigation with others in the cohort helps create lasting collaborators and friends.

Write a research proposal the first year and finish the research the second under the supervision of the chosen advisor and committee; present the research results to the committee and peers. Many students turn their RIP work into a conference paper and travel to present it.

Course work requirements are written to support the department's research philosophy. Pass up to four of the required six courses in the first two years to give time and space for immersing oneself in the chosen area.

Years three through five continue as the students go deeper and deeper into a research area and their intellectual community broadens to include collaborators from around the world. Starting in year three, the advisor funds the student's work, usually through research grants. The Preliminary exam that year is the opportunity for the student to present their research to date, to share work done by others on the topic, and to get feedback and direction for the Ph.D. from the committee, other faculty, and peers.

Most Ph.D students defend in years five and six. While Duke and the department guarantee funding through the fifth year, advisors and the department work with students to continue support for work that takes longer.

Teaching is a vital part of the Ph.D. experience. Students are required to TA for two semesters, although faculty are ready to work with students who want more involvement. The Graduate School's Certificate in College Teaching offers coursework, peer review, and evaluation of a teaching portfolio for those who want to teach. In addition, the Department awards a Certificates of Distinction in Teaching for graduating PhD students who have demonstrated excellence in and commitment to teaching and mentoring.

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Computer Science, Ph.D.

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We have a thriving Ph.D. program with approximately 80 full-time Ph.D. students hailing from all corners of the world. Most full-time Ph.D. students have scholarships that cover tuition and provide a monthly stipend. Admission is highly competitive. We seek creative, articulate students with undergraduate and master's degrees from top universities worldwide. Our  current research strengths  include data management and analysis, cybersecurity, computer games, visualization, web search, graphics, vision and image processing, and theoretical computer science.

This degree program offers interested students opportunities to do their research abroad, under the supervision of faculty at NYU Shanghai or  NYU Abu Dhabi .

• View the Computer Science Ph.D. program flyer
• Admissions requirements for the Ph.D. Program.
• Find out more about general  Admission Requirements .

To receive a Ph.D. in Computer Science at the NYU Tandon School of Engineering, a student must:

• satisfy a breadth course requirement, intended to ensure broad knowledge of computer science,
• satisfy a depth requirement, consisting of an oral qualifying exam presentation with a written report, to ensure the student's ability to do research,
• submit a written thesis proposal and make an oral presentation about the proposal,
• write a Ph.D. thesis that must be approved by a dissertation guidance committee and present an oral thesis defense, and
• satisfy all School of Engineering requirements for the Ph.D. degree, as described in the NYU Tandon School of Engineering bulletin, including graduate study duration, credit points, GPA, and time-to-degree requirements.

Upon entering the program, each student will be assigned an advisor who will guide them in formulating an individual study plan directing their course choice for the first two years. The department will hold an annual Ph.D. Student Assessment Meeting, in which all Ph.D. students will be formally reviewed.

Note: for pre-fall 2015 Ph.D. students, please see the pre-fall 2015 Ph.D. Curriculum.

Program Requirements

Details about Breadth and Depth Requirements, Thesis Proposal and Presentation, and Thesis Defense can be found in the NYU Bulletin.

Program Details

Each incoming Ph.D. student will be assigned to a research advisor, or to an interim advisor, who will provide academic advising until the student has a research advisor. The advisor will meet with the student when the student enters the program to guide the student in formulating an Individual Study Plan. The purpose of the plan is to guide the student’s course choice for the first two years in the program and to ensure that the student meets the breadth requirements. The plan may also specify additional courses to be taken by the student in order to acquire necessary background and expertise. Subsequent changes to the plan must be approved by the advisor.

Sample Plan of Study

In order to obtain a Ph.D. degree, a student must complete a minimum of 75 credits of graduate work beyond the BS degree, including at least 21 credits of dissertation. A Master of Science in Computer Science may be transferred as 30 credits without taking individual courses into consideration. Other graduate coursework in Computer Science may be transferred on a course-by-course basis. Graduate coursework in areas other than Computer Science can be transferred on a course-by-course basis with approval of the Ph.D. Committee (PHDC). The School of Engineering places some limits on the number and types of transfer credits that are available. Applications for transfer credits must be submitted for consideration before the end of the first semester of matriculation. 

All Ph.D. students will be formally reviewed each year in a Ph.D. Student Assessment Meeting. The review is conducted by the entire CSE faculty and includes at least the following items (in no particular order):

• All courses taken, grades received, and GPAs.
• Research productivity: publications, talks, software, systems, etc.
• Faculty input, especially from advisors and committee members.
• Student’s own input.
• Cumulative history of the student's progress.

As a result of the review, each student will be placed in one of the following two categories, by vote of the faculty:

• In Good Standing: The student has performed well in the previous semester and may continue in the Ph.D. program for one more year, assuming satisfactory academic progress is maintained.
• Not in Good Standing: The student has not performed sufficiently well in the previous year. The consequences of not being in good standing will vary, and may include being placed on probation, losing RA/GA/TA funding, or not being allowed to continue in the Ph.D. program.

Following the review, students will receive formal letters which will inform them of their standing. The letters may also make specific recommendations to the student as to what will be expected of them in the following year. A copy of each student’s letter will be placed in the student’s file.

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Doctoral Degree in Computer Science

Carnegie Mellon's Ph.D. in Computer Science is, above all, a research degree. When the faculty award a Ph.D., they certify that the student has a broad foundation and awareness of core concepts in computer science, has advanced the field by performing significant original research and has reported that work in a scholarly fashion.

When you begin our Ph.D. program, you’ll take the Introductory Course for Doctoral Students — an intense two week program that orients you to the department, introduces you to research and education topics our faculty are interested in, helps you find a faculty advisor and familiarizes you with Carnegie Mellon’s resources. Next, you’ll gain a broad understanding of fundamental research issues in major areas of computer science through coursework and original research. Finally, you’ll write and orally defend a thesis that guarantees you understand the area well enough to advance the state of knowledge in the field.

During the first two years of the program, you’ll gain the foundation of knowledge that will allow you to become an expert researcher in computer science, primarily by

Mastering a body of graduate material, achieved by passing 96 university units worth of graduate courses (equivalent to eight full-time courses).

Learning how to organize and begin to carry out original research, achieved by participating in directed research.

You will also serve as a teaching assistant, hone your writing and speaking skills and maintain your programming prowess. You’ll also receive periodic evaluation of your progress, and must make satisfactory progress to continue in the program.

Time Commitment:

As a Ph.D. student in computer science at CMU, you'll spend roughly five years acquiring a body of technical knowledge that includes a familiarity with the breadth of the field, as well as a deep understanding of your research area. From your second month in the program, you'll work closely with your faculty advisor, who is charged with guiding your education and monitoring your progress through the program. You'll take courses, teach and perform directed research — all to ensure that you leave Carnegie Mellon as an expert in your field. For a complete breakdown of our program requirements, read our Ph.D. Handbook .

Financial Information:

The Computer Science Department offers all Ph.D. students full financial support while they are in good academic standing in their respective programs. To learn more about Ph.D. funding, visit the SCS  Doctoral Programs  webpage.

Graduate Tuition: https://www.cmu.edu/sfs/tuition/graduate/scs.html

Student Fees: https://www.cmu.edu/sfs/tuition/fees/index.html

Carnegie Mellon Graduate Student Financial Aid: https://www.cmu.edu/sfs/financial-aid/graduate/index.html

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Computer Science is an area of study within the Harvard John A. Paulson School of Engineering and Applied Sciences. Prospective students apply through Harvard Griffin GSAS; in the online application, select “Engineering and Applied Sciences” as your program choice and select "PhD Computer Science" in the Area of Study menu.

In the Computer Science program, you will learn both the fundamentals of computation and computation’s interaction with the world. Your work will involve a wide range of areas including theoretical computer science, artificial intelligence and machine learning, economics and computer science, privacy and security, data-management systems, intelligent interfaces, operating systems, computer graphics, computational linguistics, robotics, networks, architectures, program languages, and visualization.

You will be involved with researchers in several interdisciplinary initiatives across the University, such as the Center for Research on Computation and Society, the Institute for Applied Computational Science, the Data Science Initiative, and the Berkman Klein Center for Internet and Society.

Examples of projects current and past students have worked on include leveraging machine learning to solve real-world sequential decision-making problems and using artificial intelligence to help conservation and anti-poaching efforts around the world.

Graduates of the program have gone on to a range of careers in industry in companies like Riot Games as game director and Lead Scientist at Raytheon. Others have positions in academia at University of Pittsburgh, Columbia, and Stony Brook.

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GRE General:  Not Accepted

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Academics | PhD Program

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The PhD degree is intended primarily for students who desire a career in research, advanced development, or teaching. A broad Computer Science, Engineering, Science background, intensive study, and research experience in a specialized area are the necessary requisites.

The degree of Doctor of Philosophy (PhD) is conferred on candidates who have demonstrated to the satisfaction of our Department in the following areas:

• high attainment in a particular field of knowledge, and
• the ability to do independent investigation and present the results of such research.

They must satisfy the general requirements for advanced degrees, and the program requirements specified by our Department.

Program Requirements

On average, the program is completed in five to six years, depending on the student’s research and progress.

Progress Guidelines

Students should consider the progress guidelines to ensure that they are making reasonable progress.

Monitoring Progress

Annual reviews only apply to PhD students in their second year or later; yearly meetings are held for all PhD students.

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Computer Science Ph.D. Program

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The Cornell Ph.D. program in computer science is consistently ranked among the top six departments in the country, with world-class research covering all of computer science. Our computer science program is distinguished by the excellence of the faculty, by a long tradition of pioneering research, and by the breadth of its Ph.D. program. Faculty and Ph.D. students are located both in Ithaca and in New York City at the Cornell Tech campus . The Field of Computer Science also includes faculty members from other departments (Electrical Engineering, Information Science, Applied Math, Mathematics, Operations Research and Industrial Engineering, Mechanical and Aerospace Engineering, Computational Biology, and Architecture) who can supervise a student's Ph.D. thesis research in computer science.

Over the past years we've increased our strength in areas such as artificial intelligence, computer graphics, systems, security, machine learning, and digital libraries, while maintaining our depth in traditional areas such as theory, programming languages and scientific computing.  You can find out more about our research here . 

The department provides an exceptionally open and friendly atmosphere that encourages the sharing of ideas across all areas. 

Cornell is located in the heart of the Finger Lakes region. This beautiful area provides many opportunities for recreational activities such as sailing, windsurfing, canoeing, kayaking, both downhill and cross-country skiing, ice skating, rock climbing, hiking, camping, and brewery/cider/wine-tasting. In fact, Cornell offers courses in all of these activities.

The Cornell Tech campus in New York City is located on Roosevelt Island.  Cornell Tech  is a graduate school conceived and implemented expressly to integrate the study of technology with business, law, and design. There are now over a half-dozen masters programs on offer as well as doctoral studies.

FAQ with more information about the two campuses .

Ph.D. Program Structure

Each year, about 30-40 new Ph.D. students join the department. During the first two semesters, students become familiar with the faculty members and their areas of research by taking graduate courses, attending research seminars, and participating in research projects. By the end of the first year, each student selects a specific area and forms a committee based on the student's research interests. This “Special Committee” of three or more faculty members will guide the student through to a Ph.D. dissertation. Ph.D. students that decide to work with a faculty member based at Cornell Tech typically move to New York City after a year in Ithaca.

The Field believes that certain areas are so fundamental to Computer Science that all students should be competent in them. Ph.D. candidates are expected to demonstrate competency in four areas of computer science at the high undergraduate level: theory, programming languages, systems, and artificial intelligence.

Each student then focuses on a specific topic of research and begins a preliminary investigation of that topic. The initial results are presented during a comprehensive oral evaluation, which is administered by the members of the student's Special Committee. The objective of this examination, usually taken in the third year, is to evaluate a student's ability to undertake original research at the Ph.D. level.

The final oral examination, a public defense of the dissertation, is taken before the Special Committee.

To encourage students to explore areas other than Computer Science, the department requires that students complete an outside minor. Cornell offers almost 90 fields from which a minor can be chosen. Some students elect to minor in related fields such as Applied Mathematics, Information Science, Electrical Engineering, or Operations Research. Others use this opportunity to pursue interests as diverse as Music, Theater, Psychology, Women's Studies, Philosophy, and Finance.

The computer science Ph.D. program complies with the requirements of the Cornell Graduate School , which include requirements on residency, minimum grades, examinations, and dissertation.

The Department also administers a very small 2-year Master of Science program (with thesis). Students in this program serve as teaching assistants and receive full tuition plus a stipend for their services.

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Doctor of Philosophy Program

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The Doctoral degree is awarded for superior academic and research performance. Consequently, only students who have demonstrated outstanding scholastic ability and research potential will be admitted to the academic and research program leading to the Doctorate. The program of study for the Ph.D. is to be developed by the student in close consultation with his/her academic advisor. Students are encouraged to work out their plan of study as soon as possible so that all requirements may be met.

• Program Requirements: PhD Major/Minor

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This brochure, together with the Graduate School Handbook, contains a complete description of requirements and procedures for the Ph.D. degree in Computer Science and Engineering (CSE). These requirements and the procedures for obtaining the degree are determined in part by the Graduate School, and in part by the Department. Petitions for exception to these requirements should be channeled through the departmental Graduate Studies Committee.

The material in this brochure is oriented primarily for students pursuing the Ph.D. program. Such students must be regular students, admitted to the CSE Department, and conform to Graduate School regulations; special students and students enrolled in Continuing Education must first remove any restrictions. Removal of restrictions is regulated by the Graduate School and the Departmental Graduate Studies Committee.

These procedures and requirements are subject to revision. Applicants should consult material periodically issued by the Graduate School and the Department, their advisor, or the Chair of the Graduate Studies Committee for any changes or interpretation of policy. The Graduate School also maintains a counseling office for students enrolled in Ph.D. Programs.

Program for the Doctor of Philosophy Degree in CSE

Each student entering the graduate program in Computer Science and Engineering is initially assigned a tentative academic advisor. Students' degree programs and all courses taken by students must be approved by their academic advisors. Students should consult their advisors as soon as possible after arriving on campus, and periodically thereafter until, upon developing a specific specialty interest, the student chooses a permanent advisor in that specialty area. This choice should be made as soon as practical.

The assignment of students to research advisors is performed by mutual agreement of the student and faculty member. The intent of this explicit agreement is to make students aware of the importance of early interactions with faculty in topic areas of mutual interest. Students are free to change their advisors when mutual research interests change. A "Change of Advisor" form is available through the Electronic Signature application called Docusign. The process begins by clicking on the following link which will send you an email and guide you in filling out the information needed: Change of Advisor Form .

The Graduate School rules require that advisors for students in the Ph.D. program be Category P Graduate Faculty members, but it is permissible to have a Category M Graduate Faculty member as a co-advisor. The co-advisor may be the functioning advisor. In such cases, a Category P person should be included as an integral member of the research team early in the student's research, so there is a meaningful collaboration involving the Category M functioning advisor and the Category P advisor. The Graduate School requires that the Category P advisor sign the examination and thesis approval forms.

The requirements for a Doctoral degree in Computer Science and Engineering are determined in part by general Graduate School requirements for a Ph.D. degree, and in part by specific requirements stipulated by the department. The student should refer to the Graduate School Handbook for residency requirements, regulations concerning transfer of credit from other institutions, and for credit-hour requirements stipulated by the Graduate School.

Prior to entering the first stage of study toward the Doctoral degree, a student has to successfully complete the Ph.D qualifying process (see  Section 3 ) as well as take CSE6891 (1 crhr S/U graded) during their first Autumn term.

During the first stage of Ph.D. study, the student is required to undertake a program of study in a major area and two minor areas, and to formulate a dissertation proposal. At least 10 cr-hrs of coursework in the major area and 6 cr-hrs in each of the minor areas are required. This coursework cannot include graduate core classes that were used for the qualifying process. All of the 10 credits towards the major and at least 5 credits for each minor need to be from graded graduate classes. The student's research advisor serves as the advisor for the program of study in the major area. The student, in consultation with the research advisor, chooses the two minor areas of study and the minor area advisors. The courses comprising the program of study for the minor areas must be approved by the minor area advisors.

The first stage of study toward a Doctoral degree is completed when the student has received credit for a total of 60 cr-hrs of graduate work in a program prescribed by the student's advisor and has passed the Candidacy Examination (see  Section 5 ) to be formally admitted to candidacy. At least three months prior to taking the Candidacy Examination, a proposed schedule of study should be submitted to the Chair of the Graduate Studies Committee for consideration.

The second stage is devoted primarily to research and seminars, the preparation of the dissertation, and the Final Examination (see  Section 6 ). The Final Examination is oral and deals intensively with the portion of the candidate's field of specialization in which the dissertation falls, though it need not be confined exclusively to the subject matter of the dissertation.

Overall Requirements

The CSE Ph.D. Qualifying Process consists of two components: one is coursework, and the other is research. To pass the Qualifying Process, a student needs to demonstrate satisfactory performance on both components: (1) Be competent and knowledgeable on fundamental principles of computer science and engineering, and (2) show promise for conducting original research in the areas of computer science and engineering.

For the coursework component, a student needs to achieve the average GPA of 3.3 or above on four CSE courses that include a required Algorithms course (CSE 6331) and three other courses chosen by the student in consultation with the faculty advisor  [1] . The three courses can be chosen from the seven categories listed below with at most one course from a single category. Note that a student may count one Qualifying course in this new Qualifying Process towards the major/minor course requirements in the Candidacy Exam. For the research component, a student is required to work with their faculty advisor and demonstrate satisfactory research progress  [2] .

Course Categories

The seven categories of CSE courses include: (1) Artificial Intelligence and Data Mining (CSE 6521, CSE 5523, CSE 5526, CSE 5243, CSE 5245) (2) Graphics and Visualization (CSE 5542, CSE 5543, CSE 5544, CSE 5545, CSE 5546) (3) Computer Networking (CSE 5462, CSE 5463) (4) Security and Privacy (CSE 5471, CSE5472, CSE 5473, CSE 5474) (5) Computer Systems (CSE 6431, CSE 6421, CSE 6333, CSE 5242, CSE 5441) (6) Software Engineering and Programing Languages (CSE 6341, CSE 5343) (7) Computer Theory (CSE 6321, CSE 6332, CSE 5351)

Procedures and Timeline

A Qualifying Process has two checkpoints: the first is by the end of Year 1  [2]  and the second is by the end of Year 2 [3] . In the first checkpoint, a student reports the grades of the Qualifying courses that have been taken. The student will comment on their progress towards identifying a research advisor and making research progress.

Early in the program, a student should identify research advisor(s) for the Ph.D. study. This may be the same as the initial academic advisor assigned by the Department, or a different faculty member.  The research advisor must be a member of the graduate faculty with “P” advising status in CSE. A student should declare the research advisor,  even if she or he is the same as the initial academic advisor,  by filing a Change of Advisor Form. This form is available through the Electronic Signature application called Docusign. The process begins by clicking on the following link which will send you an email and guide you in filling out the information needed: Change of Advisor Form . The research advisor will provide academic and research advice once the change of advisor form is submitted.

In the second checkpoint, a student reports the grades of the Qualifying courses that have been taken. The student’s faculty advisor will be contacted subsequently to provide input on the student’s research progress. Based on the student’s course work performance and the advisor’s research assessment, the Grad Studies Committee will notify the student of the Qualifying Process result at the second checkpoint. Both checkpoint forms can be found at  the CSE Portal .

If a student does not achieve the GPA requirement with the first four courses, a student may (a) retake the same course (required for Algorithms), (b) take a different course in the same course category, or (c) take a course in another course category.  This should be done in consultation with the faculty advisor. 

Students may file the second checkpoint form once they have achieved satisfactory performance on both coursework and research components, which could be earlier than the end of Year 2. Students should consult with their research advisor before submitting the second checkpoint form.

To maintain the status of “Good Standing” in CSE [4] , a Ph.D. student is expected to pass the Qualifying Process by the end of Year 2. Otherwise, a student who is not in good standing will not have a guaranteed appointment as a graduate teaching associate. A student who continues to not return to good standing in a timely way (e.g., by the end of the third year) may be dismissed from the Ph.D. program in Computer Science and Engineering after a conversation among the student, advisor, and graduate studies committee.

Implementation

This new Ph.D. Qualifying Process will be effective starting from  Autumn 2022.  Specifically, a student who is enrolled in the Ph.D. program of CSE in Autumn 2022 or after can only take this new Ph.D. Qualifying Process. For a smooth transition, a student who was enrolled prior to Autumn 2022 may choose to take this new Qualifying Process or the old Qualifying Exam.

Definitions and Criteria

• Faculty advisor : A student’s initial academic advisor assigned by the Department, or the research advisor chosen by the student.
• The criteria of satisfactory research performance : The most common way of satisfying this requirement is for the student to be a leading or significant contributor on a paper published, accepted, submitted, or in preparation to submit to a venue in Computer Science. Faculty advisors may provide evidence that the student has satisfied this requirement in other ways, such as making a significant contribution in research artifacts such as released software packages.
• The end of Year x : Two weeks after the end of 2*x non-summer terms since a student’s initial enrollment in the Ph.D. program of Computer Science and Engineering at Ohio State University.
• Good Standing in CSE : In addition to  the requirements from the Grad School , a Ph.D. student in CSE is required to pass the Qualifying Process by the end of Year 2. Students must also demonstrate English proficiency through one of the approved mechanisms listed on  the “English as a Second Language” website  by the end of Year 1.

Additional Notes

• While there is no accelerated option in the new policy of the PhD Qualifying Process, the accelerated option is still applicable to a student who was enrolled prior to Autumn 2022 if the student chooses to take the Qualifying Exam in the old policy.
• A student cannot transfer the credits of a Qualifying course from their prior institutes. If a student took a Qualifying course in the undergraduate program at Ohio State, the course can be counted towards the requirement of the Qualifying Process. However, the course credits cannot be counted towards their Ph.D. degree requirement except for the situations (such as the BS/MS program) allowed by the Graduate School.

The Qualifying Examination is administered Autumn and Spring semesters. Satisfactory performance on this examination, or qualification through the acceleration option listed below, is necessary for admission to the first stage of study towards the Doctoral degree.

The Qualifying Examination is based on the material covered in the graduate core areas. Specifically, students need to take the exam in algorithms (CSE 6331), either computability and unsolvability (CSE 6321) or programming languages (CSE 6341), and either operating systems (CSE 6431) or computer architecture (CSE 6421). Students who have previously studied this material are not required to take the corresponding core courses(s) in the CSE Department; they need only demonstrate their competence in these areas by satisfactory performance on the Qualifying Examination.

At the time students take the examination, they must have been admitted to the CSE Department and not be on probation. A student whose enrollment eligibility has been deactivated by the Graduate School may, if subsequently reactivated, be required to re-take the Qualifying Examination.

A student who fails the qualifying examination for the first time must retake the examination the next semester that it is offered. Students must petition the Graduate Studies Committee to retake the examination in any other semester or to retake the examination more than once.

Acceleration Option for Qualifying Exams : Students who complete the three graduate core classes (algorithms, either computability and unsolvability or programming languages, and either operating systems or computer architecture) with a GPA of 3.6 or better will be automatically granted a "conditional pass" in the qualifying examination. These students will need to demonstrate substantial research progress during their second year spring evaluation to remove the condition. One clear mechanism for demonstrating such progress is to have an accepted or submitted paper as a significant contributor, working on a project with their advisor.

Fill out the online form in the CSE Portal to apply for the Accelerate option. Advisor must approve it online.

This section further specifies the procedure set forth for the Candidacy Examination in the Graduate School Handbook. That section must be read in conjunction with this document for a full understanding of the rules governing the Candidacy Examination. The Candidacy Examination is a very important means by which the faculty can ensure that the prospective student has the necessary breadth and depth in chosen areas within computer and information science and cognate areas. The student is expected to demonstrate superior knowledge in his or her chosen areas.

To be eligible for the Candidacy Examination, the student is required to select one major area and two minor areas. The student may choose any of the pre-defined major or minor areas specified in the "Guidelines for the Ph.D. Candidacy Exam Major/Minor Areas". To demonstrate mastery in the two minor areas, the student is required to obtain a GPA of 3.3 or higher in the letter-graded courses taken in each of two minor areas. To demonstrate mastery in the major area, the student is expected to prepare a dissertation proposal. The student and the student's major advisor may suggest two examiners who are competent in the student's major area. In the Candidacy Examination, the student will be examined in written and oral format over the major area and the dissertation proposal.

The student is required to submit to the Graduate Studies Committee a proposed schedule of study for the candidacy examination at least three months in advance of the examination. The schedule should include the choice of major and minor areas, counter-signed by the student's major and two minor advisors, and the student's preliminary dissertation proposal, counter-signed by the student's major advisor and two other faculty members who will serve on the Candidacy Examination Committee. The schedule must also indicate those courses and individual studies already accomplished in each of the major and minor areas, together with additional work planned at this time. The Graduate School must be notified before the written portion of the Candidacy Examination begins. The form of the schedule of study can be  downloaded here .

After the student's proposed schedule of study has been approved by the Graduate Studies Committee, the Candidacy Examination should be scheduled in consultation with the examination committee. At least 2 weeks prior to the scheduled oral examination date, the student should declare formally the intent to take the oral portion of the Candidacy Examination. This Declaration of Intent form must be signed by the student's major advisor and the Chair of the Graduate Studies Committee before transmittal to the Dean of the Graduate School for approval.

The Examination Committee consists of at least four faculty members, including the student's major advisor, two other members of the Graduate Faculty approved by the Graduate Studies Committee for this function, and a departmental representative appointed by the Graduate Studies Committee.

The Candidacy Examination consists of two parts, namely, a written examination and an oral examination. The precise times and places of the administration of the Examination will be determined by the Examination Committee, but the entire Examination must be administered within a three-month period.

The written portion is administered and evaluated by the student's Advisory Committee. It is conducted in the following steps.

a. The student prepares a written dissertation proposal. The proposal should be concise and precise, and should include the following:

• Title and abstract
• Significance of the problem
• Scope and objectives of the research
• Methodology
• Expected results and conclusions

Students are encouraged to include in the written portion any preliminary results that support the dissertation proposal. The dissertation proposal must be submitted to all members of the Advisory Committee.

b. On receiving the dissertation proposal, the major advisor compiles a written examination for the student, taking into consideration questions posed by and comments received from the rest of the Advisory Committee.

The written examination consists of two parts. The first part asks questions related to the submitted dissertation proposal. The purpose of this part is to examine whether the dissertation proposal has substantial depth to lead to quality research and whether the student is well prepared to conduct the research outlined in the proposal. The student may be asked to revise the proposal in accordance with the suggestions received. The second part examines the student on his overall breadth and depth in his major area.

c. On receiving the written examination, the student submits written answers to the questions (and possibly a revised dissertation proposal, if so requested) to all members of the Advisory Committee.

d. The Advisory Committee evaluates the written portion including the dissertation proposal. If, based on the written portion, the Advisory Committee members see no possibility for a satisfactory overall performance on the Candidacy Examination, the Advisory Committee records an "unsatisfactory" on the Candidacy Examination report form and returns it to the Graduate School.

The oral candidacy examination shall last approximately two hours. In addition, a 30-45 minute presentation on the proposed research must be made prior to the oral examination, but after the candidate has made their written proposal available to the committee. As per Graduate School rules, the two hour oral examination is strictly an examination and may not include a formal oral presentation of the dissertation proposal. During this oral examination, the student should be prepared to defend his or her dissertation proposal and to answer questions on a range of topics including the area of specialization and general fundamentals of computer science. Examinees may use prepared slides in answering questions about their proposal. A passing grade requires a unanimous vote of the examination committee.

Notice of the time and place of both the oral portion of the Candidacy Examination and the presentation prior to that will be given to all faculty of the Department.

The student is considered to have passed the Candidacy Examination only when the decision of the Examination Committee is unanimous. The student's performance is evaluated and reported to the Graduate School as "satisfactory" (implying admission to candidacy) or "unsatisfactory" (implying failure and denial of admission to candidacy). When a failure is reported, the student may be permitted to take a second examination if recommended by the Candidacy Examination Committee. No student will be permitted to take the Candidacy Examination more than twice. The advisor is also reminded that a copy of the report to the Graduate School must be sent to the Chair of the Graduate Studies Committee for the Departmental record and student file.

After a student has passed the Candidacy Examination, the advisor of the student will nominate a Dissertation Committee to consider the merit of the dissertation. The members of the Dissertation Committee should be kept informed of the progress of research, thus giving them opportunities to make constructive suggestions for improvement of the dissertation.

The Dissertation Committee will consist of the advisor and two other members of the Graduate Faculty approved by the Graduate Studies Committee for this function. Normally, the Dissertation Committee must be approved no later than in one semester in advance of the anticipated graduation date. It is suggested that the Dissertation Committee be chosen from the committee which administered the Candidacy Examination.

The Graduate School should be consulted on the various deadlines for submission of the dissertation as well as for regulations governing the mechanics of its preparation. The Graduate School is to be informed of the Dissertation Committee members and the subject of the dissertation in the semester of expected graduation.

The Final Oral Examination is held after the approval of the draft of the dissertation by the Dissertation Committee. Generally, the Dissertation Committee and a Graduate School representative will constitute the Final Oral Examination Committee. The examination will be oral and will deal intensively with the portion of the candidate's field of specialization, though it need not be confined exclusively to the subject matter of the student's dissertation. A unanimous vote of the Final Oral Examination Committee is required for the student to pass.

It is expected that the dissertation be made available, and an announcement of the examination be made, at least one week in advance of the Final Oral Examination. The examination is open to the general public. Non-committee members should be permitted to ask questions. It is expected that the Chair of the Committee will control the ordering and kind of questions asked to ensure fairness and reasonable progress of the examination and to ensure that members of the Examination Committee have sufficient opportunity to question the candidate.

Students intending to pursue study towards a Ph.D. may apply directly to the Direct Ph.D. track. In the Direct Ph.D. track, students focus on research and study in selected areas of concentration from the beginning of their graduate studies, thereby facilitating more rapid progress towards the degree.

n addition to the standard requirements of the Ph.D. program, as detailed earlier, Direct Ph.D. students are required to satisfy the following progress requirements:

• Complete all the core courses during the first year of study and either qualify through the acceleration option, or appear for the Qualifying Examination by the first semester of the second year in the program. Students unable to meet this requirement should petition in advance to the graduate studies chair, with support of their advisor.
• Take at least 3 research cr-hrs in the form of independent study, research seminars ("Advanced Topics in ..."), or thesis research every semester, commencing from their second semester.
• Identify their research advisor and the major/minor areas of study by the end of the Spring semester of their first year (or their second semester, if they enter the Direct Ph.D. track in a different term). Students may change research advisor or major/minor areas, with the approval of the Graduate Studies Committee.

Students in the Direct Ph.D. track can obtain a Masters automatically by passing the Ph.D. Candidacy Examination. A student in the Direct Ph.D. track is not eligible to take the Department's Masters Comprehensive Examination or to apply for a Masters by writing and defending a Masters thesis. However, a student who is unable to make adequate progress in the Direct Ph.D. track after two years in the program may petition the Graduate Studies Committee to transfer to the Research (Thesis) Track of the Masters program.

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Computer Science, PhD

• Program description
• At a glance
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Algorithms, Artificial Intelligence, Big Data, Computer Science, Cybersecurity, Technology, approved for STEM-OPT extension, computing, database, enggradcs, systems

Take the next step in your journey to become an effective leader, innovator, entrepreneur or educator in your community and the world.

The PhD program in computer science prepares students to undertake fundamental and applied research in computer science. The program is available for those of high ability who seek to develop and implement their own research studies.

Students pursuing the doctorate in computer science learn to analyze, understand and apply key theories and algorithms used in the field and to generate and evaluate new theories, algorithms and software modules that can advance the field of computer science.

The program provides students with research opportunities in a wide variety of areas, including:

• artificial intelligence, machine learning and statistical modeling
• big data and data mining
• computational biology
• computer design and architecture, including nonvolatile memory computing
• computer system security, cybersecurity and cryptography
• cyber-physical systems and Internet of Things (commonly abbreviated as IoT), and robotics
• distributed computing and consensus protocols
• networking and computer systems
• novel computing paradigms (e.g., biocomputing, quantum computation)
• social computing
• theory, algorithms and optimization
• visualization and graphics

This program may be eligible for an Optional Practical Training extension for up to 36 months. This OPT work authorization term may help international students gain skills and experience in the U.S. Those interested in an OPT extension should review ASU degrees that qualify for the STEM-OPT extension at ASU's International Students and Scholars Center website.

The OPT extension only applies to students on an F-1 visa and does not apply to students completing the degree through ASU Online.

• College/school: Ira A. Fulton Schools of Engineering
• Location: Tempe

84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation

Required Core Areas (9 credit hours) foundations (3) systems (3) applications (3)

Depth (3 credit hours) three additional credit hours in one core area (3)

Research (18 credit hours) CSE 792 Research (18)

Electives and Additional Research (42 credit hours)

Culminating Experience (12 credit hours) CSE 799 Dissertation (12)

Additional Curriculum Information Courses that are used to satisfy the core area requirement cannot be used to satisfy electives or other requirements. A grade of "B" or better is required for core courses.

Eighteen credit hours of CSE 792 Research are required, and up to 54 credit hours are allowed on the plan of study. Students with research credit hours in excess of 18 add these credit hours to their electives and additional research.

Electives include:

• additional CSE 792 Research credit hours (up to 36 credit hours allowed beyond the required 18)
• computer science courses, of which up to 18 credit hours of CSE 590 and CSE 790: Reading and Conference are allowed
• up to six credit hours of interdisciplinary electives in other academic units that are subject to program chair approval

When approved by the academic unit and the Graduate College, this program allows 30 credit hours from a previously awarded master's degree to be used for this degree.

A maximum of three credit hours of 400-level coursework may be applied to the plan of study.

Applicants must fulfill the requirements of both the Graduate College and the Ira A. Fulton Schools of Engineering.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in computer science, computer engineering or a closely related area. Most applicants should have earned a master's degree, but exceptional undergraduate applicants may be admitted directly into the doctoral program.

Applicants must have a minimum cumulative GPA of 3.50 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program, or they must have a minimum cumulative GPA of 3.50 (scale is 4.00 = "A") in an applicable master's degree program.

All applicants must submit:

• graduate admission application and application fee
• official transcripts from every university attended
• three letters of recommendation
• a statement of purpose
• curriculum vitae or resume
• proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

Submission of GRE scores is optional.

Students assigned any deficiency coursework upon admission must complete those classes with a grade of "C" or higher (scale is 4.00 = "A") within two semesters of admission to the program. Deficiency courses commonly taken include:

CSE 230 Computer Organization and Assembly Language Programming CSE 310 Data Structures and Algorithms CSE 330 Operating Systems CSE 340 Principles of Programming Languages or CSE 355 Introduction to Theoretical Computer Science

The applicant's undergraduate GPA and depth of preparation in computer science and engineering are the primary factors affecting admission.

Graduates are prepared to pursue careers in research and education, including academia, government and industry.

Career examples include:

• computer science professor or researcher
• data scientist or engineer
• machine learning, AI or computer vision scientist or engineer

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Fall 2023 or Later

These guidelines apply to students who started the PhD program in the Fall 2023 or Later. For earlier guidelines, please see Fall 2015 to Spring 2023 guidelines .

1. Introduction

The PhD degree at the USC Computer Science department prepares students for a career in research. The goal of the program is to nurture talented minds via research and formal coursework, to produce future thought leaders in computer science. The program accepts students who have completed a four-year Bachelor's degree in a relevant field; a Master’s degree is not a requirement for entry. Once admitted to the program, a student must complete a set of requirements to graduate with the PhD degree. These requirements are described next.

2. Unit Requirements

A student is required to complete a total of at least 60 units, at least 40 of which must be at the 500 level or above (beyond the bachelor’s degree and including the required courses as listed in the requirements below). A student must maintain a 3.0 GPA to remain in good academic standing.

3. Course Requirement

Mandatory courses: Each student is required to complete CSCI 670 (4 units), and two semesters of CSCI 697 (1 unit each, 2 maximum). In addition, students are required to pass two semesters of CSCI 698 (1-2 units each, no maximum) as part of a teaching requirement. CSCI 698 is coursework related to a teaching requirement and is described in Section 6.

In addition to the mandatory courses, each student must complete five (5) CSCI courses at the 500 level and above, each of 4 units. No more than two (2) of these courses (8 units total) may be at the 500 level; the remaining must be CSCI courses at the 600 level. Directed Research units or thesis credits do not satisfy this requirement.

Students are strongly advised to take at least one of their elective courses in an area of Computer Science that is different from their proposed area of research. The PhD advisor is expected to provide guidance on this matter to the student. The CSCI 670 course requirement may be waived only by taking the midterm and final exams with the ongoing class (no homeworks or quizzes), and achieving satisfactory scores.

CSCI 697 and CSCI 698 may not be waived.

4. Biannual Review

Every Fall and every Spring semesters, the faculty will review each PhD student in the program. This is a rigorous review. Each student must submit a current CV and a list of publications and/or achievements. Each student’s faculty advisor will also submit a written statement assessing the student’s research and progress. The review, based on these inputs, will result in an evaluation of:

• “At or exceeds expectations”,
• “Mostly at expectations and improvements needed” (with a specific list of improvements for each student), or
• “Below expectations” (with a specific list of actions that must be taken).

After the first two semesters, students who do not have a faculty advisor will automatically receive a “Below expectations” evaluation.

Students must earn a “At or exceeds expectations” or “Mostly at expectations and improvements needed” evaluation on the most recent review before they will be allowed to take the Qualifying Examination or Dissertation Defense.

For each student who earns a “Below expectations” review, the student’s faculty advisor (if any) and the Associate Chair for PhD Affairs will develop a remediation plan to be completed within 12 months.

Two consecutive “Below expectations” reviews or failure to achieve the remediation plan may be used as grounds for removing a student from the PhD program.

5. Seminar and Thesis Proposal Attendance Requirement

Each PhD student must attend four (4) Department seminars and/or PhD Thesis Proposals each semester.

6. Teaching Requirement

All PhD students must pass CSCI 698: Teaching Practicum in two or more semesters before they can graduate with a PhD. Enrollment in CSCI 698 requires a PhD student to concurrently serve as a TA for a Computer Science or Data Science class. Every student must TA for two semesters to fulfill the teaching requirement. Every student must TA at least one undergraduate class, unless by exceptional approval by the Associate Chair for PhD Affairs.

7. Qualifying Examination

All doctoral students must pass a Qualifying Examination in Computer Science. Before passing the Qualifying Examination, students must have completed all their course requirements.

The Qualifying Examination is administered by a guidance committee consisting of the dissertation advisor and four (4) other faculty members. The student’s dissertation advisor will act as the chair of the guidance committee. The committee must include at least three (3) faculty members who have an appointment in Computer Science, and at least one committee member must be tenured in the Computer Science Department. The committee must also include one tenured/tenure track USC faculty member from another department whose primary appointment is not in Computer Science. All guidance committees must be approved by the Associate Chair for PhD Affairs, the Dean’s office, and the Graduate School. The guidance committee may include faculty from other universities, in addition to the five members from USC.

The Qualifying Examination has two parts: Written and Oral. A student must have an “At or exceeds expectations” or “Mostly at expectations and improvements needed” result from the most recent Biannual Review and at least a 3.0 GPA in order to attempt the Qualifying Examination.

Students may take the Written portion of the Qualifying Examination prior to completing their course requirements. The Written portion of the Qualifying Examination should be taken during the student’s 4th semester in the PhD program. The Written portion is in the form of a paper. Students must work with their Qualifying Examination committee to determine the topic and scope of the paper. The criteria for the paper written in fulfillment of the Written portion of the Qualifying Examination are as follows:

• Minimum 15 pages in the ACM Computer Science Style.
• Writing style must be of publishable quality, as determined by the guidance committee.
• Must include at least 30 scholarly references.

The student will pass the Written part of the Qualifying Examination with their committee’s consensus. If a student does not pass the Written portion of the Qualifying Examination, they may retake it one additional time. The student must retake the Written portion of the Qualifying Examination within at least six (6) and at most 12 months of the initial attempt.

The Oral portion of the Qualifying Examination must be taken by the end of a student’s 3rd year. It is closed to the public. The Oral portion of the Qualifying Examination will assess a student’s ability to provide a 30-minute presentation on the topic covered in the Written portion and to show adequate mastery of that topic, reflected both in the quality of the presentation and the ability to answer questions from the committee. The student will not be allowed to take the Oral portion of the Qualifying Examination without having passed the Written portion. If a student does not pass the Oral portion of the Qualifying Examination, they may retake it one additional time. The student must retake the Oral portion of the Qualifying Examination within at least six (6) and at most 12 months of the initial attempt. Postponement of any part of the Qualifying Examination will be treated on a case-by-case basis by the Associate Chair for the PhD Program.

8. Thesis Proposal

The thesis proposal presents a summary of planned future research to be carried out until graduation, contextualized by work already completed. Like for the Qualifying exam, the Thesis Proposal consists of two parts: a written part, in the same format as the written portion of the Qualifying Exam, and an oral presentation. The written part must be submitted to the student’s faculty committee at least two weeks before the scheduled oral presentation. The thesis proposal committee is administered by a guidance committee consisting of the dissertation advisor and four (4) other faculty members. The student’s dissertation advisor will act as the chair of the guidance committee. The committee must include at least three (3) faculty members who have an appointment in Computer Science, and at least one committee member must be tenured in the Computer Science Department. The committee must also include one tenured/tenure track USC faculty member from another department whose primary appointment is not in Computer Science.

The oral part of the Thesis Proposal is a presentation open to the public. The presentation must be announced at least one week in advance. The announcement must include the presentation title and abstract, the venue, date and time, as well as the names of the guidance committee members. The presentation is expected to be 45 minutes long at a minimum, with time for questions at the end. All current PhD students are encouraged to attend and participate in the public questions-and-answers session. A portion of the questions-and-answers session may be closed at the discretion of the student’s guidance committee.

The Thesis Proposal must be made by the end of a student’s 5th year in the program, although it is strongly recommended that students do so by the end of their 4th year. Only students who have passed the Qualifying examination (both Written and Oral), may schedule a Thesis Proposal presentation. The guidance committee will assess the thesis proposal for novelty, substance, and feasibility, and decide whether to approve the proposal. If a student’s Thesis Proposal is not approved, the student may make one additional proposal. The student must make the additional Thesis Proposal within at most six (6) months of the first attempt.

9. Dissertation and Defense

A dissertation involving original research completes the requirements for a PhD degree. A Defense of the dissertation must be held as a public oral examination. The Defense must be announced at least one week in advance. To schedule the Defense, the student must have passed the Thesis Proposal. The Defense announcement must include the dissertation title and abstract, the venue, date and time for the examination, as well as the names of the dissertation Defense committee members.

The student must provide the complete written dissertation to the committee at least five (5) business days before the scheduled defense.

The dissertation defense committee must have at least three (3) members, of which at least two must have an appointment in Computer Science. The student’s dissertation advisor will chair the committee. At least one committee member must be tenured in the Computer Science Department. The committee must also include one tenured (or tenure-track) USC faculty member from another department whose primary appointment is not in Computer Science.

Specific upload deadlines and instructions can be found on the USC Graduate School Thesis Center website https://graduateschool.usc.edu/current-students/thesis-dissertation-submission/ .

10. Time Limits

A student must pass the Qualifying Examination within four years of being admitted to the PhD program. The dissertation Defense must be completed within seven years of being admitted to the PhD program (six if the student arrives with a relevant Master’s degree).

After seven years in the PhD program, the student may not be eligible for any Teaching Assistantship funding from the Computer Science department. An extension to either time limit (Qualifying Exam and Defense) requires approval of two-thirds of the Computer Science faculty. In no case may the granted extensions exceed the time limits set by the USC Graduate School.

11. Absences

Doctoral students may be granted a maximum of 24 months (not necessarily consecutive) leave-of-absence by the Department Chair, or by a committee appointed by the Department Chair with the approval of the Graduate School. During these absences, the clock defining the time limits for the qualifying and defense examinations is suspended. The clock is resumed when the student returns from the leave-of-absence. Any leaves longer than 24 months, or leave applied for within four months of the expiration of a time limit, requires an approval of two-thirds of Computer Science faculty. Absences longer than 24 months also require USC Graduate School approval.

12. Transfer Requirements

Students with a relevant MS degree from another university may transfer up to 30 units towards their PhD degree. At most two courses may be substituted for the allowed two 500- level courses in the course requirement. No substitutions are allowed for the 600-level courses.

13. Petitioning for MS Degree

After satisfying the PhD course requirements and completing a minimum of 28 units with a GPA of 3.0, a current CS PhD is eligible to petition for a Master's degree in Computer Science.  This will require the approval of your faculty advisor(s) and the Associate Chair of the department.  The docusign process can be found here .

14. Existing Students and the new PhD Requirements

These requirements shall apply to all students admitted to the Computer Science PhD program for Fall 2023 or thereafter. Students admitted prior to Fall 2023 may choose to have these requirements applied to them. To do this, the student must submit to the Department an approval letter signed by the student’s PhD advisor.

Published on November 22nd, 2022

Last updated on November 2nd, 2023

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The CS Policies/Procedures Manual is online and is incorporated in the CS Grad website. The website contains all current information on the CS policies/procedures, in addition to other helpful information and links. 

The Purdue Graduate School manual contains the minimum requirements, but CS policies may exceed the Grad School requirements and are considered the primary policy to follow in those situations.

The doctoral program is designed to prepare students for a career in computer science research. The program includes coursework to provide core computer science knowledge, coursework to provide knowledge in the intended area of research, and extensive research training and experience.

Invitation to participate:

Information Session on the CS Doctoral Requirements with the Graduate Study Chair

Thursday, September 14th, at 5:30 pm in LWSN 3102

The doctoral program requirements are:

• One research orientation course
• Ethics Training
• Two initial research courses
• Core course requirement
• Advisory Committee
• Area-specific requirements
• Research credits
• Preliminary Examination
• Annual Review

Graduation Candidacy Information

Changes in Requirements

Policies and Procedures Manual

Sample Ph.D. Timeline

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1. Research Orientation

The research orientation requirement consists of three parts: (a) the research orientation course, (b) the ethics training, and (c) the initial research courses.

a. Research Orientation Course

Students must, in their first year, take “ CS 59100 Research Seminar for First-Year Graduate Students ”.  This course introduces students to the research of CS faculty and includes lectures on how to conduct, present, and review research.

b. Ethics Training

Students must complete this multiple part training in the first year.

• Students must be present for the ethics lecture that is part of " CS 59100 Research Seminar for First-Year Graduate Students ".
• Go to  CITI Program: Responsible Conduct of Research .
• Register with “Purdue University” as your Organization Affiliation;
• Complete the  Course Responsible Conduct of Research Training – Faculty, Postdoctoral, and Graduate Course
• Forward the certificate of completion to the Graduate Office by email at  [email protected]
• participation  in discussions with colleagues on RCR topics related to their specific research programs (e.g., through group meetings, coursework, orientations, professional development activities, or other organized events.) OR
• participation/viewing panel discussions around topics identified as most relevant by the College of Science researchers. There will be a one hour event each spring semester to fulfill this. These will be announced by the Grad Office whenever available.
• Each student researcher is responsible for self-reporting their activities here: https://webapps.ecn.purdue.edu/VPR/RT/login

Further information on Responsible Conduct of Research

c. Initial Research Courses

Students must take two initial research courses by the end of their third semester.  Students take an initial research course by registering for at least 3 credits of “CS 69900, Research PhD Thesis”. To register for research, use the Scheduling Assistant in myPurdue. Only one initial research course can be taken per semester or per summer.  Each student must identify a faculty supervisor and work with that faculty supervisor to define and conduct a research project. At the end of each course, the student must write a report that is formally evaluated by the faculty supervisor. The two initial courses may be supervised by the same or by different faculty members .

Beginning PhD students (in first two years) doing research with a faculty member other than their initial advisor may discuss whether to formally change the advisor of record. If both the initial advisor and the proposed new initial advisor agree, an email to the grad office can be sent to request an update. Email confirmation from both advisors is needed before myCS can be updated. Students in their third year and beyond should have a plan of study approved identifying their permanent advisor. See Plan of study below for additional details. 

2. Core Course Requirement

Students must satisfy this requirement by the end of their fourth semester by passing one theory core course and one systems core course with an average grade of at least 3.5.

The theory core course must be chosen from the following set: {“CS 58000 Algorithm Design, Analysis, And Implementation”, “CS 58400 Theory of Computation and Computational Complexity”, "CS 58800 Randomized Algorithms"}.

The systems core course must be chosen from the following set: {“CS 50300 Operating Systems”, “CS 50500 Distributed Systems”, “CS 53600 Data Communication and Computer Networks”}.

For the purpose of this requirement, a grade of A+, A, A-, B+, B, and B- counts as 4.3, 4.0, 3.7, 3.3, 3.0, and 2.7, respectively, must be earned.

3. Plan of Study

Students must submit a draft plan of study by the end of the fifth week of their fifth semester (not including summer semesters), and are expected to revise it and to submit as final, as directed by the CS Graduate Office, by the end of classes that semester. The plan of study lists (a) the student’s advisory committee, and (b) the courses the student plans to use to fulfill the degree requirement. The draft of the plan of study is submitted electronically and must be approved by the student's advisory committee and by the CS Graduate Committee, see Instructions for Filing a Plan of Study .

a. Advisory Committee

The student must identify a Ph.D. research supervisor and then consult with the research supervisor to define an advisory committee. The advisory committee consists of

• the student’s research supervisor (a.k.a. “major professor”, or “advisor”), who serves as chair.
• two or more additional faculty members.
• a research supervisor who is not a CS faculty member may be approved as a co-chair along with a co-chair from CS.
• a majority of committee members must be CS faculty . Faculty from other Purdue West Lafayette departments may be approved to serve on the committee.
• committee members from outside Purdue West Lafayette may be approved, but they must be in addition to the required three committee members from Purdue West Lafayette.

The plan of study must include at least six graduate level CS courses and only CS graduate courses, with a grade point average (GPA) of at least 3.5. The six courses must be taught by a faculty member whose primary appointment is in the CS department. The courses must include the two courses used to satisfy the core course requirement. The remaining courses must be three-credit, level 50000 or 60000, non-individual CS courses. CS 50100, 50010, 50011 and certain CS 59000/69000/59200/59300 courses may not be used.

Students admitted to the doctoral program Fall 2017 or later may list at most one approved variable title CS 59000/69000/59200/59300 lecture course. Please check the Variable Title Courses page to determine if a course has been approved for inclusion on a PhD plan of study.

All courses included in the plan of study must have a student evaluation component, and they must be graded in the usual manner so they can be used to compute the GPA. In particular, courses graded on a pass/no pass or satisfactory/unsatisfactory basis cannot be included in the plan of study. A student receiving a grade lower than C- in a course on the plan of study will have to repeat or replace the course. If a course is repeated, only the last grade, even if lower, is used to compute all GPAs involving that course.

Courses taken as a graduate student from other institutions may be accepted with the approval of the student's advisory committee , the Graduate Committee, and the Graduate School.  The minimum acceptable grade for such courses is B- or the equivalent. Please refer to these  Instructions for Transfer of Courses (PDF).  Requests must be submitted to the CS grad office within the first six weeks of the fall or spring semester.

The courses on the plan of study cannot have been used to satisfy requirements for an undergraduate degree, nor can they cause the student's doctoral plan of study to include courses used for more than one master's degree.

4. Area-Specific Requirements

Students must satisfy any additional requirements specific to their area of research . Students must consult with their major professor to ascertain area-specific requirements.  Students are responsible for knowing and completing area-specific requirements by the assigned deadlines.

5. Research

Ph.D. research experience is planned, supervised, accumulated, and demonstrated by forming an advisory committee , by taking graduate level computer science courses , by conducting thesis research, by passing a preliminary examination, and by writing and defending a thesis.

a. Research Credits

The credits used to satisfy the Ph.D. degree credit requirement consist of (1) all credits for the courses that appear on the plan of study, and (2) all “CS 69900 Research Ph.D. Thesis” credit hours with a grade of S. At least 90 total credit hours are required. For example, if a plan of study lists 18 credits, an additional 72 research credits of CS 69900 with a grade of S are required.

At least one-third (i.e. 30) of the total credit hours used to satisfy the Ph.D. degree credit hour requirement must be earned while registered for doctoral study at Purdue West Lafayette.

b. Preliminary Examination

Students must pass a preliminary examination that tests competence in the student’s research area and readiness for research on a specific problem. The content of the examination is at the discretion of the examining committee. The examination may include a presentation by the student of papers relevant to a chosen research topic, an oral examination over advanced material on the student’s research topic, a presentation by the student of the student’s preliminary research results, or a proposal of thesis research.

The examining committee consists of the student's advisory committee , and of an additional member, who is not on the advisory committee, who is approved by the Graduate Committee.

The preliminary examination can be taken as soon as the plan of study is approved, and as late as two semesters before the semester in which the thesis defense is held. The student should consult with their advisory committee to decide when to take the preliminary examination (e.g. if a final exam is taken Fall 2021, the prelim exam would have needed to have happened Fall 2020). 

Please see the Procedure for Arranging a Preliminary Examination.

The thesis must present new results worthy of publication. At least two academic sessions of registration devoted to research and writing must elapse between the preliminary and final doctoral examinations. The student must defend the thesis publicly and to the satisfaction of the examining committee, which consists of the student's  advisory committee  and of one additional faculty member who represents an area outside that of the thesis, and who is approved by the graduate committee.

The thesis should be defended at the latest by the end of the fourth semester following the one in which the student passes the  preliminary examination .

Defense Procedure Instructions

Thesis Format

In preparing a PhD dissertation, please read the graduate school templates information at:  http://www.purdue.edu/gradschool/research/thesis/templates.html  and choose the LaTeX Template. For the review of the format, schedule a Formatting Consultation prior to your defense at  https://www.purdue.edu/gradschool/research/thesis/appointment.html  .

Thesis Deposit Process

6. Annual Review

Each doctoral students’ academic and research progress is evaluated annually by their major professor and the Graduate Committee.  Students receive written feedback and guidance to support progress.

The Ph.D. requirements described above apply to all students entering or re-entering the Department of Computer Science at West Lafayette ("the Department") as degree-seeking graduate students in the summer session of 2016 or later. Here is an archive of the 2013 , 2010 ,  2009 ,  2006 ,  2002  and  2001  Doctoral Program Requirements.

Students are governed by the degree requirements in effect when they enter the Department as degree-seeking students.  For students re-entering, the date of the most recent re-entry determines the degree requirements.  Students who wish to take advantage of subsequent changes may apply to the Graduate Committee to be governed by all degree requirements in effect at a specified subsequent time.  Choosing features from different sets of requirements is not permitted.

For information about the commencement ceremony, please visit www.purdue.edu/commencement .

In order to graduate, you must declare candidacy for the semester in which you intend to graduate by the designated deadline. You declare candidacy by using the Scheduling Assistant within myPurdue and registering for either CAND 99100, 99200, or 99300 (Form 23 is no longer used). 

If you are declaring candidacy for multiple degrees (both PhD and MS) within the same semester, please register for candidacy for one degree, and then contact [email protected],  to let them know information on the second degree. Candidacy will only show on your schedule for one degree, but we will work with the Registrar's Office and Grad School to make sure expectation for both degrees is recorded in their systems.

CAND 99100: This the candidacy to register for if you are currently taking any courses and/or research. Doctoral students must register for research in proportion to their efforts during each session, and must be registered for at least one credit of research in this semester. Research registration should be commensurate with actual research and writing efforts. (International students registering for candidacy and less than full-time, need to request approval for a Reduced Course Load from ISS; at least one credit if not funded or at least three credits if funded.)

Special candidate registration may be allowed for those students needing to only deposit (CAND 992) or defend/deposit (CAND 993). If allowed, please note:

• Early deadlines apply (you can find the deadline calendar on the Grad School website,   https://www.purdue.edu/gradschool , and select Academic Calendar).
• Students cannot be registered for any credits in this semester (research or coursework).
• Students MUST be registered in research the semester prior to enrolling in one of these candidate types (including summer if research (which includes writing/formatting thesis) was performed).
• Students may still hold an RA appointment (and TA appointment, if remaining for the full semester despite defending and/or depositing early).
• Candidates who register for this special registration and who do not meet the early deadline, will be switched by the Grad School to CAND 991 and required to register for credits.  If you’re funded or on Research in Absentia, you need to make sure you are funded for a minimum of 3 credits, so check your schedule if you miss the early deadline and notify  [email protected]  immediately to assist you with modifying the number of registered credits.

CAND 993  (Exam-Only Candidacy): Candidacy for those that ONLY need to defend AND deposit their thesis.  Please note that there is a fee to register in CAND 993.

PLEASE NOTE: Being registered as a candidate does not automatically register you for the commencement ceremony itself. If you plan to participate in commencement, you must respond by using the Commencement tab on myPurdue. It will be added to your myPurdue account after a specified date in the semester you have registered as a candidate.

Graduation Deadline Calendar: https://www.purdue.edu/gradschool/about/calendar/deadlines.html

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Sample Ph.D timeline

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Berkeley Berkeley Academic Guide: Academic Guide 2023-24

Computer science.

About the Program

The Department of Electrical Engineering and Computer Sciences (EECS) offers two graduate programs in Computer Science: the Master of Science (MS), and the Doctor of Philosophy (PhD).

Master of Science (MS)

The Master of Science (MS) emphasizes research preparation and experience and, for most students, is a chance to lay the groundwork for pursuing a PhD.

Doctor of Philosophy (PhD)

The Berkeley PhD in EECS combines coursework and original research with some of the finest EECS faculty in the US, preparing for careers in academia or industry. Our alumni have gone on to hold amazing positions around the world.

Visit Department Website

Admission to the University

Applying for graduate admission.

Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. A complete list of graduate academic departments, degrees offered, and application deadlines can be found on the Graduate Division website .

Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application can be found on the Graduate Division website .

Admission Requirements

The minimum graduate admission requirements are:

A bachelor’s degree or recognized equivalent from an accredited institution;

A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and

Enough undergraduate training to do graduate work in your chosen field.

For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page . It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here .

Where to apply?

Visit the Berkeley Graduate Division application page .

Admission to the Program

The following items are required for admission to the Berkeley EECS MS/PhD program in addition to the University’s general graduate admissions requirements:

• Statement of Purpose: Why are you applying for this program? What will do you plan to accomplish during this degree program? What do you want to do afterward, and how will this degree help you reach that goal?
• Personal History Statement: What experiences from your past made you decide to go into this field? And how will your personal history help you succeed in this program and your future goals?
• GPA: If you attended a university outside the USA, please leave the GPA section blank.
• Resume: Please also include a full resume/CV listing your experience and education.

Complete the online UC Berkeley graduate application:

• Start your application through this link , and fill in each relevant page.
• Upload the materials above, and send the recommender links several weeks prior to the application deadline to give your recommenders time to submit their letters.

Doctoral Degree Requirements

Normative time requirements.

Normative time in the EECS department is between 5.5-6 years for the doctoral program.

Time to Advancement

The faculty of the College of Engineering recommends a minimum number of courses taken while in graduate standing. The total minimum is 24 units of coursework, taken for a letter grade and not including 297, 298, 299, 301, 375 and 602.

Preliminary Exams

The EECS preliminary requirement consists of two components.

Oral Examination

The oral exam serves an advisory role in a student's graduate studies program, giving official feedback from the exam committee of faculty members. Students must be able to demonstrate an integrated grasp of the exam area's body of knowledge in an unstructured framework. Students must pass the oral portion of the preliminary exam within their first two attempts. A third attempt is possible with a petition of support from the student's faculty adviser and final approval by the prelim committee chair. Failure to pass the oral portion of the preliminary exam will result in the student being ineligible to complete the PhD program. The examining committee awards a score in the range of 0-10. The minimum passing score is 6.0.

Breadth Courses

The breadth courses ensure that students have exposure to areas outside of their concentration. It is expected that students will achieve high academic standards in these courses.

CS students must complete courses from three of the following areas, passing each with at least a B+. One course must be selected from the Theory, AI, or Graphics/HCI group; and one course must be selected from the Programming, Systems, or Architecture/VLSI group 1 .

COMPSCI 260B ,  COMPSCI 263 , and  EL ENG 219C  cannot be used to fulfill this constraint, though they can be used to complete one of the three courses.

Qualifying Examination (QE)

The QE is an important checkpoint meant to show that a student is on a promising research track toward the PhD degree. It is a University examination, administered by the Graduate Council, with the specific purpose of demonstrating that "the student is clearly an expert in those areas of the discipline that have been specified for the examination, and that he or she can, in all likelihood, design and produce an acceptable dissertation." Despite such rigid criteria, faculty examiners recognize that the level of expertise expected is that appropriate for a third year graduate student, who may be only in the early stages of a research project.

The EECS Department offers the qualifying exam in two formats: A or B. Students may choose the exam type of their choice after consultation with their adviser.

• Students prepare a write-up and presentation, summarizing a specific research area, preferably the one in which they intend to do their dissertation work. Their summary surveys that area and describes open and interesting research problems.
• They describe why they chose these problems and indicate what direction their research may take in the future.
• They prepare to display expertise on both the topic presented and on any related material that the committee thinks is relevant.
• The student should talk (at least briefly) about any research progress they have made to date (e.g., MS project, PhD research, or class project). Some evidence of their ability to do research is expected.
• The committee shall evaluate students on the basis of their comprehension of the fundamental facts and principles that apply within their research area and students' ability to think incisively and critically about the theoretical and practical aspects of the chosen field.
•  Students must demonstrate command of the content and the ability to design and produce an acceptable dissertation.

This option includes the presentation and defense of a thesis proposal in addition to the requirements of format A. It will include a summary of research to date and plans for future work (or at least the next stage thereof). The committee shall not only evaluate the student's thesis proposal and their progress to date but shall also evaluate according to format A. As in format A, students should prepare a single document and presentation, but in this case, additional emphasis must be placed on research completed to date and plans for the remainder of the dissertation research.

Thesis Proposal Defense

Students not presenting a satisfactory thesis proposal defense, either because they took format A for the QE or because the material presented in a format B exam was not deemed a satisfactory proposal defense (although it may have sufficed to pass the QE), must write up and present a thesis proposal, which should include a summary of the student's research to date and plans for the remainder of the dissertation research. Students should be prepared to discuss background and related areas, but the focus of the proposal should be on the progress made so far, and detailed plans for completing the thesis. The standard for continuing with PhD research is that the proposal has sufficient merit to lead to a satisfactory dissertation. Another purpose of this presentation is for faculty to provide feedback on the quality of work to date. For this step, the committee should consist of at least three members from EECS familiar with the research area, preferably including those on the dissertation committee.

Normative Time in Candidacy

Advancement to candidacy.

Students must file the advancement form in the Graduate Office by no later than the end of the semester following the one in which the qualifying exam was passed. In approving this application, Graduate Division approves the dissertation committee and will send a certificate of candidacy.

Students in the EECS department are required to be advanced to candidacy at least two semesters before they are eligible to graduate.  Once a student is advanced to candidacy, candidacy is valid for five years.  For the first three years, non-resident tuition may be waived, if applicable.

Dissertation Talk

As part of the requirements for the doctoral degree, students must give a public talk on the research covered by their dissertation. The dissertation talk should be given a few months before the signing of the final submission of the dissertation. It must be given before the final submission of the dissertation.  The talk should cover all major components of the dissertation work in a substantial manner; in particular, the dissertation talk should not omit topics that will appear in the dissertation but are incomplete at the time of the talk.

The dissertation talk is to be attended by the whole dissertation committee, or, if this is not possible, by at least a majority of the members. Attendance at this talk is part of the committee's responsibility. It is, however, the responsibility of the student to schedule a time for the talk that is convenient for members of the committee. The EECS department requires that the talk be given during either the fall or spring semester.

Required Professional Development

Graduate student instructor teaching requirement.

The EECS department requires all PhD candidates to serve as Graduate Student Instructors (GSIs) within the EECS department. The GSI teaching requirement not only helps to develop a student's communication skills, but it also makes a great contribution to the department's academic community. Students must fulfill this requirement by working as a GSI (excluding  EL ENG 375 or COMPSCI 375 ) for a total of 30 hours minimum prior to graduation. At least 20 of those hours must be for an EE or CS undergraduate course. In addition, students must earn a Satisfactory grade in the mandatory pedagogy course to complete the GSI teaching requirement.

Master's Degree Requirements

Unit requirements.

A minimum of 24 units is required.

All courses must be taken for a letter grade, except for courses numbered  299, which are only offered for  S/U  credit.

Students must maintain a minimum cumulative GPA of 3.0. No credit will be given for courses in which the student earns a grade of D+ or below.

Transfer credit may be awarded for a maximum of four semester or six quarter units of graduate coursework from another institution.

For both Plan I and Plan II, MS students need to complete the departmental Advance to Candidacy form, have their research advisor sign the form, and submit the form to the Department's Master's Degree Advisor. Students who choose Plan I will also need to complete the Graduate Division's online Advancement to Candidacy form through  Calcentral  no later than the end of the second week of classes in their final semester. 

Once a student has advanced to candidacy, candidacy is valid for three years.

Capstone/Thesis (Plan I)

Students planning to use Plan I for their MS Degree will need to follow the  Graduate Division's “Thesis Filing Guidelines."  A copy of the signature page and abstract should be submitted to the Department's Master's Degree Advisor.  In addition, a copy should be uploaded to  the EECS website .

Capstone/Master's Project (Plan II)

Students planning to use Plan II for their MS Degree will need to produce an MS Plan II Title/Signature Page. A copy of the signature page and abstract should be submitted to the the Department's Master's Degree Advisor. In addition, a copy should be uploaded to  the EECS website .

There is no special formatting required for the body of the Plan II MS report, unlike the Plan I MS thesis, which must follow Graduate Division guidelines.

Select a subject to view courses

Electrical engineering and computer sciences, electrical engineering, eecs c206a introduction to robotics 4 units.

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course is an introduction to the field of robotics. It covers the fundamentals of kinematics, dynamics, control of robot manipulators, robotic vision, sensing, forward & inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics, & control. We will present techniques for geometric motion planning & obstacle avoidance. Open problems in trajectory generation with dynamic constraints will also be discussed. The course also presents the use of the same analytical techniques as manipulation for the analysis of images & computer vision. Low level vision, structure from motion, & an introduction to vision & learning will be covered. The course concludes with current applications of robotics. Introduction to Robotics: Read More [+]

Rules & Requirements

Prerequisites: Familiarity with linear algebra at level of EECS 16A / EECS 16B or MATH 54 . Experience doing coding in python at the level of COMPSCI 61A . Preferred: experience developing software at level of COMPSCI 61B and experience using Linux. EECS 120 is not required, but some knowledge of linear systems may be helpful for the control of robots

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and three hours of laboratory per week.

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructors: Sastry, Sreenath

Formerly known as: Electrical Engin and Computer Sci 206A

Also listed as: MEC ENG C206A

Introduction to Robotics: Read Less [-]

EECS C206B Robotic Manipulation and Interaction 4 Units

Terms offered: Spring 2024, Spring 2023 This course is a sequel to EECS C106A /206A, which covers kinematics, dynamics and control of a single robot. This course will cover dynamics and control of groups of robotic manipulators coordinating with each other and interacting with the environment. Concepts will include an introduction to grasping and the constrained manipulation, contacts and force control for interaction with the environment. We will also cover active perception guided manipulation, as well as the manipulation of non-rigid objects. Throughout, we will emphasize design and human-robot interactions, and applications to applications in manufacturing, service robotics, tele-surgery, and locomotion. Robotic Manipulation and Interaction: Read More [+]

Prerequisites: Students are expected to have taken EECS C106A / BioE C106A / ME C106A / ME C206A/ EECS C206A or an equivalent course. A strong programming background, knowledge of Python and Matlab, and some coursework in feedback controls (such as EE C128 / ME C134) are also useful. Students who have not taken EECS C106A / BioE C106A / ME C106A / ME C206A/ EECS C206A should have a strong programming background, knowledge of Python and Matlab, and exposure to linear algebra, and Lagrangian dynamics

Instructors: Bajcsy, Sastry

Formerly known as: Electrical Engin and Computer Sci 206B

Also listed as: MEC ENG C206B

Robotic Manipulation and Interaction: Read Less [-]

EECS 208 Computational Principles for High-dimensional Data Analysis 4 Units

Terms offered: Fall 2023, Fall 2022, Fall 2021 Introduction to fundamental geometric and statistical concepts and principles of low-dimensional models for high-dimensional signal and data analysis, spanning basic theory, efficient algorithms, and diverse real-world applications. Systematic study of both sampling complexity and computational complexity for sparse, low-rank, and low-dimensional models – including important cases such as matrix completion, robust principal component analysis, dictionary learning, and deep networks. Computational Principles for High-dimensional Data Analysis: Read More [+]

Prerequisites: The following courses are recommended undergraduate linear algebra (Math 110), statistics (Stat 134), and probability (EE126). Back-ground in signal processing (ELENG 123), optimization (ELENG C227T), machine learning (CS189/289), and computer vision ( COMPSCI C280 ) may allow you to appreciate better certain aspects of the course material, but not necessary all at once. The course is open to senior undergraduates, with consent from the instructor

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructor: Ma

Computational Principles for High-dimensional Data Analysis: Read Less [-]

EECS 219A Numerical Simulation and Modeling 4 Units

Terms offered: Spring 2024 Numerical simulation and modeling are enabling technologies that pervade science and engineering. This course provides a detailed introduction to the fundamental principles of these technologies and their translation to engineering practice. The course emphasizes hands-on programming in MATLAB and application to several domains, including circuits, nanotechnology, and biology. Numerical Simulation and Modeling: Read More [+]

Prerequisites: Consent of instructor; a course in linear algebra and on circuits is very useful

Credit Restrictions: Students will receive no credit for EL ENG 219A after completing EL ENG 219.

Fall and/or spring: 15 weeks - 4 hours of lecture per week

Additional Format: Four hours of lecture per week.

Instructor: Roychowdhury

Formerly known as: Electrical Engineering 219A

Numerical Simulation and Modeling: Read Less [-]

EECS 219C Formal Methods: Specification, Verification, and Synthesis 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Introduction to the theory and practice of formal methods for the design and analysis of systems, with a focus on algorithmic techniques. Covers selected topics in computational logic and automata theory including modeling and specification formalisms, temporal logics, satisfiability solving, model checking, synthesis, learning, and theorem proving. Applications to software and hardware design, cyber-physical systems, robotics, computer security , and other areas will be explored as time permits. Formal Methods: Specification, Verification, and Synthesis: Read More [+]

Prerequisites: Graduate standing or consent of instructor; COMPSCI 170 is recommended

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week.

Instructor: Seshia

Formerly known as: Electrical Engineering 219C

Formal Methods: Specification, Verification, and Synthesis: Read Less [-]

EECS 225A Statistical Signal Processing 3 Units

Terms offered: Spring 2023, Fall 2021, Fall 2020 This course connects classical statistical signal processing (Hilbert space filtering theory by Wiener and Kolmogorov, state space model, signal representation, detection and estimation, adaptive filtering) with modern statistical and machine learning theory and applications. It focuses on concrete algorithms and combines principled theoretical thinking with real applications. Statistical Signal Processing: Read More [+]

Prerequisites: EL ENG 120 and EECS 126

Additional Format: Three hours of Lecture per week for 15 weeks.

Instructors: Jiao, Waller

Formerly known as: Electrical Engineering 225A

Statistical Signal Processing: Read Less [-]

EECS 225B Digital Image Processing 3 Units

Terms offered: Fall 2023, Fall 2022, Fall 2020 This course deals with computational methods as applied to digital imagery. It focuses on image sensing and acquisition, image sampling and quantization; spatial transformation, linear and nonlinear filtering; introduction to convolutional neural networks, and GANs; applications of deep learning methods to image processing problems; image enhancement, histogram equalization, image restoration, Weiner filtering, tomography, image reconstruction from projections and partial Fourier information, Radon transform, multiresolution analysis, continuous and discrete wavelet transform and computation, subband coding, image and video compression, sparse signal approximation, dictionary techniques, image and video compression standards, and more. Digital Image Processing: Read More [+]

Prerequisites: Basic knowledge of signals and systems, convolution, and Fourier Transform

Instructor: Zakhor

Formerly known as: Electrical Engineering 225B

Digital Image Processing: Read Less [-]

EECS 227AT Optimization Models in Engineering 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization. Optimization Models in Engineering: Read More [+]

Prerequisites: MATH 54 or consent of instructor

Credit Restrictions: Students will receive no credit for EECS 227AT after taking EECS 127 or Electrical Engineering 127/227AT.

Instructor: El Ghaoui

Formerly known as: Electrical Engineering 227AT

Optimization Models in Engineering: Read Less [-]

EECS C249B Cyber Physical System Design Prinicples and Applications 4 Units

Terms offered: Spring 2020, Spring 2019, Spring 2016 Principles of embedded system design. Focus on design methodologies and foundations. Platform-based design and communication-based design and their relationship with design time, re-use, and performance. Models of computation and their use in design capture, manipulation, verification, and synthesis. Mapping into architecture and systems platforms. Performance estimation. Scheduling and real-time requirements. Synchronous languages and time-triggered protocols to simplify the design process. Cyber Physical System Design Prinicples and Applications: Read More [+]

Prerequisites: Suggested but not required: CS170, EECS149/249A

Credit Restrictions: Students will receive no credit for EECS C249B after completing EL ENG 249, or EECS 249B. A deficient grade in EECS C249B may be removed by taking EECS 249B.

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 2 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and two hours of laboratory per week.

Instructor: Sangiovanni-Vincentelli

Formerly known as: Electrical Engineering C249B/Civil and Environmental Engineering C289

Also listed as: CIV ENG C289

Cyber Physical System Design Prinicples and Applications: Read Less [-]

EECS 251A Introduction to Digital Design and Integrated Circuits 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 An introduction to digital circuit and system design. The material provides a top-down view of the principles, components, and methodologies for large scale digital system design. The underlying CMOS devices and manufacturing technologies are introduced, but quickly abstracted to higher levels to focus the class on design of larger digital modules for both FPGAs (field programmable gate arrays) and ASICs (application specific integrated circuits). The class includes extensive use of industrial grade design automation and verification tools for assignments, labs, and projects. Introduction to Digital Design and Integrated Circuits: Read More [+]

Objectives & Outcomes

Course Objectives: The Verilog hardware description language is introduced and used. Basic digital system design concepts, Boolean operations/combinational logic, sequential elements and finite-state-machines, are described. Design of larger building blocks such as arithmetic units, interconnection networks, input/output units, as well as memory design (SRAM, Caches, FIFOs) and integration are also covered. Parallelism, pipelining and other micro-architectural optimizations are introduced. A number of physical design issues visible at the architecture level are covered as well, such as interconnects, power, and reliability.

Student Learning Outcomes: Although the syllabus is the same as EECS151, the assignments and exams for EECS251A will have harder problems that test deeper understanding expected from a graduate level course.

Prerequisites: EECS 16A and EECS 16B ; COMPSCI 61C ; and recommended: EL ENG 105 . Students must enroll concurrently in at least one the laboratory flavors EECS 251LA or EECS 251LB . Students wishing to take a second laboratory flavor next term can sign-up only for that laboratory section and receive a letter grade. The prerequisite for “Lab-only” enrollment that term will be EECS 251A from previous terms

Credit Restrictions: Students must enroll concurrently in at least one the laboratory flavors Electrical Engineering and Computer Science 251LA or Electrical Engineering and Computer Science 251LB. Students wishing to take a second laboratory flavor next term can sign-up only for that laboratory section and receive a letter grade. The pre-requisite for “Lab-only” enrollment that term will be Electrical Engineering and Computer Science 251A from previous terms.

Instructors: Stojanovic, Wawrzynek

Formerly known as: Electrical Engineering 241A

Introduction to Digital Design and Integrated Circuits: Read Less [-]

EECS 251B Advanced Digital Integrated Circuits and Systems 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course aims to convey a knowledge of advanced concepts of digital circuit and system-on-a-chip design in state-of-the-art technologies. Emphasis is on the circuit and system design and optimization for both energy efficiency and high performance for use in a broad range of applications, from edge computing to datacenters. Special attention will be devoted to the most important challenges facing digital circuit designers in the coming decade. The course is accompanied with practical laboratory exercises that introduce students to modern tool flows. Advanced Digital Integrated Circuits and Systems: Read More [+]

Prerequisites: Introduction to Digital Design and Integrated Circuits, EECS151 (taken with either EECS151LA or EECS151LB lab) or EECS251A (taken with either EECS251LA or EECS251LB lab)

Credit Restrictions: Students will receive no credit for EECS 251B after completing COMPSCI 250 , or EL ENG 241B .

Fall and/or spring: 15 weeks - 4 hours of lecture and 1 hour of discussion per week

Additional Format: Four hours of lecture and one hour of discussion per week.

Instructors: Nikolić, Shao, Wawrzynek, Asanović, Stojanović, Seshia

Advanced Digital Integrated Circuits and Systems: Read Less [-]

EECS 251LA Introduction to Digital Design and Integrated Circuits Lab 2 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This lab lays the foundation of modern digital design by first presenting the scripting and hardware description language base for specification of digital systems and interactions with tool flows. The labs are centered on a large design with the focus on rapid design space exploration. The lab exercises culminate with a project design, e.g. implementation of a 3-stage RISC-V processor with a register file and caches. The design is mapped to simulation and layout specification. Introduction to Digital Design and Integrated Circuits Lab: Read More [+]

Course Objectives: Software testing of digital designs is covered leading to a set of exercises that cover the design flow. Digital synthesis, floor-planning, placement and routing are covered, as well as tools to evaluate timing and power consumption. Chip-level assembly is covered, including instantiation of custom blocks: I/O pads, memories, PLLs, etc.

Student Learning Outcomes: Although the syllabus is the same as EECS151LA, the assignments and exams for EECS251LA will have harder problems in labs and in the project that test deeper understanding expected from a graduate level course.

Prerequisites: EECS 16A , EECS 16B , and COMPSCI 61C ; and EL ENG 105 is recommended

Fall and/or spring: 15 weeks - 3 hours of laboratory per week

Additional Format: Three hours of laboratory per week.

Introduction to Digital Design and Integrated Circuits Lab: Read Less [-]

EECS 251LB Introduction to Digital Design and Integrated Circuits Lab 2 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This lab covers the design of modern digital systems with Field-Programmable Gate Array (FPGA) platforms. A series of lab exercises provide the background and practice of digital design using a modern FPGA design tool flow. Digital synthesis, partitioning, placement, routing, and simulation tools for FPGAs are covered in detail. The labs exercises culminate with a large design project, e.g., an implementation of a full 3-stage RISC-V processor system, with caches, graphics acceleration, and external peripheral components. The design is mapped and demonstrated on an FPGA hardware platform. Introduction to Digital Design and Integrated Circuits Lab: Read More [+]

Student Learning Outcomes: Although the syllabus is the same as EECS151LB, the assignments and exams for EECS251LB will have harder problems in labs and in the project that test deeper understanding expected from a graduate level course.

COMPSCI C200A Principles and Techniques of Data Science 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023, Spring 2023, Spring 2022, Spring 2021, Spring 2020 Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project. Principles and Techniques of Data Science: Read More [+]

Prerequisites: COMPSCI C8 / INFO C8 / STAT C8 or ENGIN 7 ; and either COMPSCI 61A or COMPSCI 88. Corequisites: MATH 54 or EECS 16A

Credit Restrictions: Students will receive no credit for DATA C200 \ COMPSCI C200A \ STAT C200C after completing DATA C100 .

Fall and/or spring: 8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week 15 weeks - 3-3 hours of lecture, 1-1 hours of discussion, and 0-1 hours of laboratory per week

Summer: 8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and zero to one hours of laboratory per week. Six hours of lecture and two hours of discussion and zero to two hours of laboratory per week for 8 weeks. Six hours of lecture and two hours of discussion and zero to two hours of laboratory per week for 8 weeks.

Subject/Course Level: Computer Science/Graduate

Formerly known as: Statistics C200C/Computer Science C200A

Also listed as: DATA C200/STAT C200C

Principles and Techniques of Data Science: Read Less [-]

COMPSCI C249A Introduction to Embedded Systems 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course introduces students to the basics of models, analysis tools, and control for embedded systems operating in real time. Students learn how to combine physical processes with computation. Topics include models of computation, control, analysis and verification, interfacing with the physical world, mapping to platforms, and distributed embedded systems. The course has a strong laboratory component, with emphasis on a semester-long sequence of projects. Introduction to Embedded Systems: Read More [+]

Credit Restrictions: Students will receive no credit for Electrical Engineering/Computer Science C249A after completing Electrical Engineering/Computer Science C149.

Fall and/or spring: 15 weeks - 3 hours of lecture and 3 hours of laboratory per week

Additional Format: Three hours of lecture and three hours of laboratory per week.

Instructors: Lee, Seshia

Formerly known as: Electrical Engineering C249M/Computer Science C249M

Also listed as: EL ENG C249A

Introduction to Embedded Systems: Read Less [-]

COMPSCI 250 VLSI Systems Design 4 Units

Terms offered: Fall 2020, Spring 2017, Spring 2016 Unified top-down and bottom-up design of integrated circuits and systems concentrating on architectural and topological issues. VLSI architectures, systolic arrays, self-timed systems. Trends in VLSI development. Physical limits. Tradeoffs in custom-design, standard cells, gate arrays. VLSI design tools. VLSI Systems Design: Read More [+]

Prerequisites: COMPSCI 150

Fall and/or spring: 15 weeks - 3 hours of lecture and 4 hours of laboratory per week

Additional Format: Three hours of lecture and four hours of laboratory per week.

Instructor: Wawrzynek

VLSI Systems Design: Read Less [-]

COMPSCI 252A Graduate Computer Architecture 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Graduate survey of contemporary computer organizations covering: early systems, CPU design, instruction sets, control, processors, busses, ALU, memory, I/O interfaces, connection networks, virtual memory, pipelined computers, multiprocessors, and case studies. Term paper or project is required. Graduate Computer Architecture: Read More [+]

Prerequisites: COMPSCI 61C

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week

Additional Format: Three hours of lecture and two hours of discussion per week.

Instructors: Asanović, Kubiatowicz

Formerly known as: Computer Science 252

Graduate Computer Architecture: Read Less [-]

COMPSCI 260A User Interface Design and Development 4 Units

Terms offered: Spring 2024, Spring 2023, Fall 2020 The design, implementation, and evaluation of user interfaces. User-centered design and task analysis. Conceptual models and interface metaphors. Usability inspection and evaluation methods. Analysis of user study data. Input methods (keyboard, pointing, touch, tangible) and input models. Visual design principles. Interface prototyping and implementation methodologies and tools. Students will develop a user interface for a specific task and target user group in teams. User Interface Design and Development: Read More [+]

Prerequisites: COMPSCI 61B , COMPSCI 61BL , or consent of instructor

Credit Restrictions: Students will receive no credit for Computer Science 260A after taking Computer Science 160.

Instructors: Agrawala, Canny, Hartmann

User Interface Design and Development: Read Less [-]

COMPSCI 260B Human-Computer Interaction Research 3 Units

Terms offered: Fall 2024, Fall 2017 This course is a broad introduction to conducting research in Human-Computer Interaction. Students will become familiar with seminal and recent literature; learn to review and critique research papers; re-implement and evaluate important existing systems; and gain experience in conducting research. Topics include input devices, computer-supported cooperative work, crowdsourcing, design tools, evaluation methods, search and mobile interfaces, usable security , help and tutorial systems. Human-Computer Interaction Research: Read More [+]

Prerequisites: COMPSCI 160 recommended, or consent of instructor

Instructor: Hartmann

Human-Computer Interaction Research: Read Less [-]

COMPSCI 261 Security in Computer Systems 3 Units

Terms offered: Fall 2023, Spring 2021, Fall 2018 Graduate survey of modern topics in computer security, including protection, access control, distributed access security, firewalls, secure coding practices, safe languages, mobile code, and case studies from real-world systems. May also cover cryptographic protocols, privacy and anonymity, and/or other topics as time permits. Security in Computer Systems: Read More [+]

Prerequisites: COMPSCI 162

Instructors: D. Song, Wagner

Security in Computer Systems: Read Less [-]

COMPSCI 261N Internet and Network Security 4 Units

Terms offered: Spring 2020, Fall 2016, Spring 2015 Develops a thorough grounding in Internet and network security suitable for those interested in conducting research in the area or those more broadly interested in security or networking. Potential topics include denial-of-service; capabilities; network intrusion detection/prevention; worms; forensics; scanning; traffic analysis; legal issues; web attacks; anonymity; wireless and networked devices; honeypots; botnets; scams; underground economy; attacker infrastructure; research pitfalls. Internet and Network Security: Read More [+]

Prerequisites: EL ENG 122 or equivalent; and COMPSCI 161 or familiarity with basic security concepts

Instructor: Paxson

Internet and Network Security: Read Less [-]

COMPSCI 262A Advanced Topics in Computer Systems 4 Units

Terms offered: Fall 2023, Fall 2022, Fall 2021 Graduate survey of systems for managing computation and information, covering a breadth of topics: early systems; volatile memory management, including virtual memory and buffer management; persistent memory systems, including both file systems and transactional storage managers; storage metadata, physical vs. logical naming, schemas, process scheduling, threading and concurrency control; system support for networking, including remote procedure calls, transactional RPC, TCP, and active messages; security infrastructure; extensible systems and APIs; performance analysis and engineering of large software systems. Homework assignments, exam, and term paper or project required. Advanced Topics in Computer Systems: Read More [+]

Prerequisites: COMPSCI 162 and entrance exam

Instructors: Brewer, Hellerstein

Formerly known as: 262

Advanced Topics in Computer Systems: Read Less [-]

COMPSCI 262B Advanced Topics in Computer Systems 3 Units

Terms offered: Spring 2020, Spring 2009, Fall 2008 Continued graduate survey of large-scale systems for managing information and computation. Topics include basic performance measurement; extensibility, with attention to protection, security, and management of abstract data types; index structures, including support for concurrency and recovery; parallelism, including parallel architectures, query processing and scheduling; distributed data management, including distributed and mobile file systems and databases; distributed caching; large-scale data analysis and search. Homework assignments, exam, and term paper or project required. Advanced Topics in Computer Systems: Read More [+]

Prerequisites: COMPSCI 262A

Instructors: Brewer, Culler, Hellerstein, Joseph

COMPSCI 263 Design of Programming Languages 3 Units

Terms offered: Fall 2021, Fall 2019, Spring 2019 Selected topics from: analysis, comparison, and design of programming languages, formal description of syntax and semantics, advanced programming techniques, structured programming, debugging, verification of programs and compilers, and proofs of correctness. Design of Programming Languages: Read More [+]

Prerequisites: COMPSCI 164

Instructor: Necula

Design of Programming Languages: Read Less [-]

COMPSCI 264 Implementation of Programming Languages 4 Units

Terms offered: Fall 2023, Fall 2021, Spring 2011 Compiler construction. Lexical analysis, syntax analysis. Semantic analysis code generation and optimization. Storage management. Run-time organization. Implementation of Programming Languages: Read More [+]

Prerequisites: COMPSCI 164 ; COMPSCI 263 recommended

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 6 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and six hours of laboratory per week.

Instructor: Bodik

Implementation of Programming Languages: Read Less [-]

COMPSCI 265 Compiler Optimization and Code Generation 3 Units

Terms offered: Fall 2024, Fall 2009, Spring 2003 Table-driven and retargetable code generators. Register management. Flow analysis and global optimization methods. Code optimization for advanced languages and architectures. Local code improvement. Optimization by program transformation. Selected additional topics. A term paper or project is required. Compiler Optimization and Code Generation: Read More [+]

Instructor: Sen

Compiler Optimization and Code Generation: Read Less [-]

COMPSCI C267 Applications of Parallel Computers 3 - 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022, Spring 2021 Models for parallel programming. Overview of parallelism in scientific applications and study of parallel algorithms for linear algebra, particles, meshes, sorting, FFT, graphs, machine learning, etc. Survey of parallel machines and machine structures. Programming shared- and distributed-memory parallel computers, GPUs, and cloud platforms. Parallel programming languages, compilers, libraries and toolboxes. Data partitioning techniques. Techniques for synchronization and load balancing. Detailed study and algorithm/program development of medium sized applications. Applications of Parallel Computers: Read More [+]

Prerequisites: No formal pre-requisites. Prior programming experience with a low-level language such as C, C++, or Fortran is recommended but not required. CS C267 is intended to be useful for students from many departments and with different backgrounds, although we will assume reasonable programming skills in a conventional (non-parallel) language, as well as enough mathematical skills to understand the problems and algorithmic solutions presented

Repeat rules: Course may be repeated for credit without restriction.

Fall and/or spring: 15 weeks - 3-3 hours of lecture and 1-1 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of laboratory per week.

Instructors: Demmel, Yelick

Also listed as: ENGIN C233

Applications of Parallel Computers: Read Less [-]

COMPSCI W267 Applications of Parallel Computers 3 Units

Terms offered: Prior to 2007 Parallel programming, from laptops to supercomputers to the cloud. Goals include writing programs that run fast while minimizing programming effort. Parallel architectures and programming languages and models, including shared memory (eg OpenMP on your multicore laptop), distributed memory (MPI and UPC on a supercomputer), GPUs (CUDA and OpenCL), and cloud (MapReduce, Hadoop and Spark). Parallel algorithms and software tools for common computations (eg dense and sparse linear algebra, graphs, structured grids). Tools for load balancing, performance analysis, debugging. How high level applications are built (eg climate modeling). On-line lectures and office hours. Applications of Parallel Computers: Read More [+]

Student Learning Outcomes: An understanding of computer architectures at a high level, in order to understand what can and cannot be done in parallel, and the relative costs of operations like arithmetic, moving data, etc. To master parallel programming languages and models for different computer architectures To recognize programming "patterns" to use the best available algorithms and software to implement them. To understand sources of parallelism and locality in simulation in designing fast algorithms

Prerequisites: Computer Science W266 or the consent of the instructor

Credit Restrictions: Students will receive no credit for Computer Science W267 after completing Computer Science C267.

Fall and/or spring: 15 weeks - 3 hours of web-based lecture per week

Additional Format: Three hours of web-based lecture per week.

Online: This is an online course.

COMPSCI 268 Computer Networks 3 Units

Terms offered: Spring 2023, Spring 2021, Spring 2019 Distributed systems, their notivations, applications, and organization. The network component. Network architectures. Local and long-haul networks, technologies, and topologies. Data link, network, and transport protocols. Point-to-point and broadcast networks. Routing and congestion control. Higher-level protocols. Naming. Internetworking. Examples and case studies. Computer Networks: Read More [+]

Instructors: Joseph, Katz, Stoica

Formerly known as: 292V

Computer Networks: Read Less [-]

COMPSCI 270 Combinatorial Algorithms and Data Structures 3 Units

Terms offered: Fall 2024, Spring 2023, Spring 2021 Design and analysis of efficient algorithms for combinatorial problems. Network flow theory, matching theory, matroid theory; augmenting-path algorithms; branch-and-bound algorithms; data structure techniques for efficient implementation of combinatorial algorithms; analysis of data structures; applications of data structure techniques to sorting, searching, and geometric problems. Combinatorial Algorithms and Data Structures: Read More [+]

Prerequisites: COMPSCI 170

Instructors: Papadimitriou, Rao, Sinclair, Vazirani

Combinatorial Algorithms and Data Structures: Read Less [-]

COMPSCI 271 Randomness and Computation 3 Units

Terms offered: Fall 2024, Fall 2022, Spring 2020 Computational applications of randomness and computational theories of randomness. Approximate counting and uniform generation of combinatorial objects, rapid convergence of random walks on expander graphs, explicit construction of expander graphs, randomized reductions, Kolmogorov complexity, pseudo-random number generation, semi-random sources. Randomness and Computation: Read More [+]

Prerequisites: COMPSCI 170 and at least one course from the following: COMPSCI 270 - COMPSCI 279

Instructor: Sinclair

Randomness and Computation: Read Less [-]

COMPSCI 272 Foundations of Decisions, Learning, and Games 4 Units

Terms offered: Not yet offered This course introduces students to the mathematical foundation of learning in the presence of strategic and societal agency. This is a theory-oriented course that will draw from the statistical and computational foundations of machine learning, computer science, and economics. As a research-oriented course, a range of advanced topics will be explored to paint a comprehensive picture of classical and modern approaches to learning for the purpose of decision making.These topics include foundations of learning, foundations of algorithmic game theory, cooperative and non-cooperative games, equilibria and dynamics, learning in games, information asymmetries, mechanism design, and learning with incentives. Foundations of Decisions, Learning, and Games: Read More [+]

Prerequisites: Graduate-level mathematical maturity, including proof-based graduate-level courses in at least two, but recommended three, of the following categories: Statistics and Probability, e.g., STAT205A, STAT210B Economics, e.g., ECON207A Algorithms, e.g., CS270 Optimization, e.g., EE 227B Control theory, e.g., EE 221A

Credit Restrictions: Students will receive no credit for COMPSCI 272 after completing COMPSCI 272 . A deficient grade in COMPSCI 272 may be removed by taking COMPSCI 272 .

Instructors: Jordan, Haghtalab

Foundations of Decisions, Learning, and Games: Read Less [-]

COMPSCI 273 Foundations of Parallel Computation 3 Units

Terms offered: Spring 2012, Fall 2010, Spring 2009 . Fundamental theoretical issues in designing parallel algorithms and architectures. Shared memory models of parallel computation. Parallel algorithms for linear algegra, sorting, Fourier Transform, recurrence evaluation, and graph problems. Interconnection network based models. Algorithm design techniques for networks like hypercubes, shuffle-exchanges, threes, meshes and butterfly networks. Systolic arrays and techniques for generating them. Message routing. Foundations of Parallel Computation: Read More [+]

Prerequisites: COMPSCI 170 , or consent of instructor

Instructor: Rao

Foundations of Parallel Computation: Read Less [-]

COMPSCI 274 Computational Geometry 3 Units

Terms offered: Spring 2019, Spring 2017, Spring 2015 . Constructive problems in computational geometry: convex hulls, triangulations, Voronoi diagrams, arrangements of hyperplanes; relationships among these problems. Search problems: advanced data structures; subdivision search; various kinds of range searches. Models of computation; lower bounds. Computational Geometry: Read More [+]

Instructor: Shewchuk

Computational Geometry: Read Less [-]

COMPSCI 276 Cryptography 3 Units

Terms offered: Fall 2024, Fall 2020, Fall 2018 Graduate survey of modern topics on theory, foundations, and applications of modern cryptography. One-way functions; pseudorandomness; encryption; authentication; public-key cryptosystems; notions of security. May also cover zero-knowledge proofs, multi-party cryptographic protocols, practical applications, and/or other topics, as time permits. Cryptography: Read More [+]

Instructors: Trevisan, Wagner

Cryptography: Read Less [-]

COMPSCI 278 Machine-Based Complexity Theory 3 Units

Terms offered: Spring 2024, Spring 2021, Fall 2016 Properties of abstract complexity measures; Determinism vs. nondeterminism; time vs. space; complexity hierarchies; aspects of the P-NP question; relative power of various abstract machines. Machine-Based Complexity Theory: Read More [+]

Prerequisites: 170

Instructor: Trevisan

Machine-Based Complexity Theory: Read Less [-]

COMPSCI 280A Intro to Computer Vision and Computational Photography 4 Units

Terms offered: Fall 2024, Fall 2023 This course introduces students to computing with visual data (images and video). We will cover acquisition, representation, and manipulation of visual information from digital photographs (image processing), image analysis and visual understanding (computer vision), and image synthesis (computational photography). Key algorithms will be presented, ranging from classical to contemporary, with an emphasis on using these techniques to build practical systems. The hands-on emphasis will be reflected in the programming assignments, where students will acquire their own images and develop, largely from scratch, image analysis and synthesis tools for real-world applications. Intro to Computer Vision and Computational Photography: Read More [+]

Course Objectives: Students will learn classic algorithms in image manipulation with Gaussian and Laplacian Pyramids, understand the hierarchy of image transformations including homographies, and how to warp an image with these transformations., Students will learn how to apply Convolutional Neural Networks for computer vision problems and how they can be used for image manipulation. Students will learn the fundamentals of 3D vision: stereo, multi-view geometry, camera calibration, structure-frommotion, multi-view stereo, and the plenoptic function mechanics of a pin-hole camera, representation of images as pixels, physics of light and the process of image formation, to manipulating the visual information using signal processing techniques in the spatial and frequency domains.

Student Learning Outcomes: After this class, students will be comfortable implementing, from scratch, these algorithms in modern programming languages and deep learning libraries.

Prerequisites: COMPSCI 61B and MATH 53 . MATH 54 , MATH 56 , MATH 110 , or EECS 16A . COMPSCI 182 or COMPSCI 189

Instructors: Efros, Kanazawa

Intro to Computer Vision and Computational Photography: Read Less [-]

COMPSCI C280 Computer Vision 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Paradigms for computational vision. Relation to human visual perception. Mathematical techniques for representing and reasoning, with curves, surfaces and volumes. Illumination and reflectance models. Color perception. Image segmentation and aggregation. Methods for bottom-up three dimensional shape recovery: Line drawing analysis, stereo, shading, motion, texture. Use of object models for prediction and recognition. Computer Vision: Read More [+]

Prerequisites: MATH 1A ; MATH 1B ; MATH 53 ; and MATH 54 (Knowledge of linear algebra and calculus)

Instructor: Malik

Also listed as: VIS SCI C280

Computer Vision: Read Less [-]

COMPSCI C281A Statistical Learning Theory 3 Units

Terms offered: Fall 2023, Fall 2021, Fall 2020 Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods. Statistical Learning Theory: Read More [+]

Instructors: Bartlett, Jordan, Wainwright

Also listed as: STAT C241A

Statistical Learning Theory: Read Less [-]

COMPSCI C281B Advanced Topics in Learning and Decision Making 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning. Advanced Topics in Learning and Decision Making: Read More [+]

Also listed as: STAT C241B

Advanced Topics in Learning and Decision Making: Read Less [-]

COMPSCI 282A Designing, Visualizing and Understanding Deep Neural Networks 4 Units

Terms offered: Fall 2023, Spring 2023, Fall 2022 Deep Networks have revolutionized computer vision, language technology, robotics and control. They have growing impact in many other areas of science and engineering. They do not however, follow a closed or compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground. Designing, Visualizing and Understanding Deep Neural Networks: Read More [+]

Student Learning Outcomes: Students will come to understand visualizing deep networks. Exploring the training and use of deep networks with visualization tools. Students will learn design principles and best practices: design motifs that work well in particular domains, structure optimization and parameter optimization. Understanding deep networks. Methods with formal guarantees: generative and adversarial models, tensor factorization.

Prerequisites: MATH 53 and MATH 54 or equivalent; COMPSCI 70 or STAT 134 ; COMPSCI 61B or equivalent; COMPSCI 189 or COMPSCI 289A (recommended)

Instructor: Canny

Designing, Visualizing and Understanding Deep Neural Networks: Read Less [-]

COMPSCI 284A Foundations of Computer Graphics 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Techniques of modeling objects for the purpose of computer rendering: boundary representations, constructive solids geometry, hierarchical scene descriptions. Mathematical techniques for curve and surface representation. Basic elements of a computer graphics rendering pipeline; architecture of modern graphics display devices. Geometrical transformations such as rotation, scaling, translation, and their matrix representations. Homogeneous coordinates, projective and perspective transformations. Foundations of Computer Graphics: Read More [+]

Prerequisites: COMPSCI 61B or COMPSCI 61BL ; programming skills in C, C++, or Java; linear algebra and calculus; or consent of instructor

Credit Restrictions: Students will receive no credit for Computer Science 284A after taking 184.

Instructors: Agrawala, Barsky, O'Brien, Ramamoorthi, Sequin

Foundations of Computer Graphics: Read Less [-]

COMPSCI 284B Advanced Computer Graphics Algorithms and Techniques 4 Units

Terms offered: Spring 2024, Spring 2022, Spring 2019 This course provides a graduate-level introduction to advanced computer graphics algorithms and techniques. Students should already be familiar with basic concepts such as transformations, scan-conversion, scene graphs, shading, and light transport. Topics covered in this course include global illumination, mesh processing, subdivision surfaces, basic differential geometry, physically based animation, inverse kinematics, imaging and computational photography, and precomputed light transport. Advanced Computer Graphics Algorithms and Techniques: Read More [+]

Prerequisites: COMPSCI 184

Instructors: O'Brien, Ramamoorthi

Formerly known as: Computer Science 283

Advanced Computer Graphics Algorithms and Techniques: Read Less [-]

COMPSCI 285 Deep Reinforcement Learning, Decision Making, and Control 3 Units

Terms offered: Fall 2023, Fall 2022, Fall 2021 Intersection of control, reinforcement learning, and deep learning. Deep learning methods, which train large parametric function approximators, achieve excellent results on problems that require reasoning about unstructured real-world situations (e.g., computer vision, speech recognition, NLP). Advanced treatment of the reinforcement learning formalism, the most critical model-free reinforcement learning algorithms (policy gradients, value function and Q-function learning, and actor-critic), a discussion of model-based reinforcement learning algorithms, an overview of imitation learning, and a range of advanced topics (e.g., exploration, model-based learning with video prediction, transfer learning, multi-task learning, and meta-learning). Deep Reinforcement Learning, Decision Making, and Control: Read More [+]

Student Learning Outcomes: Provide an opportunity to embark on a research-level final project with support from course staff. Provide hands-on experience with several commonly used RL algorithms; Provide students with an overview of advanced deep reinforcement learning topics, including current research trends; Provide students with foundational knowledge to understand deep reinforcement learning algorithms;

Prerequisites: CS189/289A or equivalent is a prerequisite for the course. This course will assume some familiarity with reinforcement learning, numerical optimization and machine learning, as well as a basic working knowledge of how to train deep neural networks (which is taught in CS182 and briefly covered in CS189)

Instructors: Levine, Abbeel

Deep Reinforcement Learning, Decision Making, and Control: Read Less [-]

COMPSCI 286 Implementation of Data Base Systems 3 Units

Terms offered: Fall 2009, Spring 2009, Spring 2008 Implementation of data base systems on modern hardware systems. Considerations concerning operating system design, including buffering, page size, prefetching, etc. Query processing algorithms, design of crash recovery and concurrency control systems. Implementation of distributed data bases and data base machines. Implementation of Data Base Systems: Read More [+]

Prerequisites: COMPSCI 162 and COMPSCI 186 ; or COMPSCI 286A

Instructors: Franklin, Hellerstein

Formerly known as: Computer Science 286B

Implementation of Data Base Systems: Read Less [-]

COMPSCI 286A Introduction to Database Systems 4 Units

Terms offered: Spring 2018, Fall 2017, Spring 2017 Access methods and file systems to facilitate data access. Hierarchical, network, relational, and object-oriented data models. Query languages for models. Embedding query languages in programming languages. Database services including protection, integrity control, and alternative views of data. High-level interfaces including application generators, browsers, and report writers. Introduction to transaction processing. Database system implementation to be done as term project. Introduction to Database Systems: Read More [+]

Prerequisites: COMPSCI 61B and COMPSCI 61C

Credit Restrictions: Students will receive no credit for CS 286A after taking CS 186.

Introduction to Database Systems: Read Less [-]

COMPSCI 287 Advanced Robotics 3 Units

Terms offered: Fall 2019, Fall 2015, Spring 2015 Advanced topics related to current research in algorithms and artificial intelligence for robotics. Planning, control, and estimation for realistic robot systems, taking into account: dynamic constraints, control and sensing uncertainty, and non-holonomic motion constraints. Advanced Robotics: Read More [+]

Prerequisites: Instructor consent for undergraduate and masters students

Instructor: Abbeel

Advanced Robotics: Read Less [-]

COMPSCI 287H Algorithmic Human-Robot Interaction 4 Units

Terms offered: Spring 2023, Spring 2021, Spring 2020 As robot autonomy advances, it becomes more and more important to develop algorithms that are not solely functional, but also mindful of the end-user. How should the robot move differently when it's moving in the presence of a human? How should it learn from user feedback? How should it assist the user in accomplishing day to day tasks? These are the questions we will investigate in this course. We will contrast existing algorithms in robotics with studies in human-robot interaction, discussing how to tackle interaction challenges in an algorithmic way, with the goal of enabling generalization across robots and tasks. We will also sharpen research skills: giving good talks, experimental design, statistical analysis, literature surveys. Algorithmic Human-Robot Interaction: Read More [+]

Student Learning Outcomes: Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to apply Bayesian inference and learning techniques to enhance coordination in collaborative tasks. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to apply optimization techniques to generate motion for HRI. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to contrast and relate model-based and model-free learning from demonstration. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to develop a basic understanding of verbal and non-verbal communication. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to ground algorithmic HRI in the relvant psychology background. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to tease out the intricacies of developing algorithms that support HRI. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to analyze and diagram the literature related to a particular topic. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to communicate scientific content to a peer audience. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to critique a scientific paper's experimental design and analysis.

Instructor: Dragan

Algorithmic Human-Robot Interaction: Read Less [-]

COMPSCI 288 Natural Language Processing 4 Units

Terms offered: Fall 2024, Fall 2023, Spring 2023 Methods and models for the analysis of natural (human) language data. Topics include: language modeling, speech recognition, linguistic analysis (syntactic parsing, semantic analysis, reference resolution, discourse modeling), machine translation, information extraction, question answering, and computational linguistics techniques. Natural Language Processing: Read More [+]

Prerequisites: COMPSCI 188 ; and COMPSCI 170 is recommended

Instructor: Klein

Natural Language Processing: Read Less [-]

COMPSCI 289A Introduction to Machine Learning 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This course provides an introduction to theoretical foundations, algorithms, and methodologies for machine learning, emphasizing the role of probability and optimization and exploring a variety of real-world applications. Students are expected to have a solid foundation in calculus and linear algebra as well as exposure to the basic tools of logic and probability, and should be familiar with at least one modern, high-level programming langua ge. Introduction to Machine Learning: Read More [+]

Prerequisites: MATH 53 , MATH 54 , COMPSCI 70 , and COMPSCI 188 ; or consent of instructor

Credit Restrictions: Students will receive no credit for Comp Sci 289A after taking Comp Sci 189.

Instructors: Listgarten, Malik, Recht, Sahai, Shewchuk

Introduction to Machine Learning: Read Less [-]

COMPSCI 294 Special Topics 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Topics will vary from semester to semester. See Computer Science Division announcements. Special Topics: Read More [+]

Fall and/or spring: 4 weeks - 3-15 hours of lecture per week 6 weeks - 3-9 hours of lecture per week 8 weeks - 2-6 hours of lecture per week 10 weeks - 2-5 hours of lecture per week 15 weeks - 1-3 hours of lecture per week

Additional Format: One to three hours of lecture per week for standard offering. In some instances, condensed special topics classes running from 2-10 weeks may also be offered usually to accommodate guest instructors. Total works hours will remain the same but more work in a given week will be required.

Special Topics: Read Less [-]

COMPSCI 297 Field Studies in Computer Science 12.0 Units

Terms offered: Fall 2022, Spring 2016, Fall 2015 Supervised experience in off-campus companies relevant to specific aspects and applications of electrical engineering and/or computer science. Written report required at the end of the semester. Field Studies in Computer Science: Read More [+]

Fall and/or spring: 15 weeks - 1-12 hours of independent study per week

Summer: 6 weeks - 1-30 hours of independent study per week 8 weeks - 1.5-22.5 hours of independent study per week 10 weeks - 1-18 hours of independent study per week

Additional Format: Independent study. Independent study.

Grading: Offered for satisfactory/unsatisfactory grade only.

Field Studies in Computer Science: Read Less [-]

COMPSCI 298 Group Studies Seminars, or Group Research 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Advanced study in various subjects through seminars on topics to be selected each year, informal group studies of special problems, group participation in comprehensive design problems, or group research on complete problems for analysis and experimentation. Group Studies Seminars, or Group Research: Read More [+]

Repeat rules: Course may be repeated for credit without restriction. Students may enroll in multiple sections of this course within the same semester.

Fall and/or spring: 15 weeks - 1-4 hours of lecture per week

Additional Format: One to four hours of lecture per week.

Grading: The grading option will be decided by the instructor when the class is offered.

Group Studies Seminars, or Group Research: Read Less [-]

COMPSCI 299 Individual Research 1 - 12 Units

Terms offered: Fall 2023, Fall 2022, Summer 2017 Second 6 Week Session Investigations of problems in computer science. Individual Research: Read More [+]

Fall and/or spring: 15 weeks - 0-1 hours of independent study per week

Summer: 6 weeks - 8-30 hours of independent study per week 8 weeks - 6-22.5 hours of independent study per week 10 weeks - 1.5-18 hours of independent study per week

Additional Format: Independent study. Forty-five hours of work per unit per term.

Individual Research: Read Less [-]

COMPSCI 300 Teaching Practice 1 - 6 Units

Terms offered: Fall 2012, Fall 2011, Spring 2011 Supervised teaching practice, in either a one-on-one tutorial or classroom discussion setting. Teaching Practice: Read More [+]

Fall and/or spring: 15 weeks - 0 hours of independent study per week

Summer: 6 weeks - 1-5 hours of independent study per week 8 weeks - 1-4 hours of independent study per week

Additional Format: Three to twenty hours of discussion and consulting per week.

Subject/Course Level: Computer Science/Professional course for teachers or prospective teachers

Teaching Practice: Read Less [-]

COMPSCI 302 Designing Computer Science Education 3 Units

Terms offered: Spring 2023, Spring 2022, Spring 2021 Discussion and review of research and practice relating to the teaching of computer science: knowledge organization and misconceptions, curriculum and topic organization, evaluation, collaborative learning, technology use, and administrative issues. As part of a semester-long project to design a computer science course, participants invent and refine a variety of homework and exam activities, and evaluate alternatives for textbooks, grading and other administrative policies, and innovative uses of technology. Designing Computer Science Education: Read More [+]

Prerequisites: COMPSCI 301 and two semesters of GSI experience

Fall and/or spring: 15 weeks - 2 hours of lecture per week

Additional Format: Two hours of lecture per week.

Instructor: Garcia

Designing Computer Science Education: Read Less [-]

COMPSCI 365 Introduction to Instructional Methods in Computer Science for Academic Interns 2 - 4 Units

Terms offered: Not yet offered This is a course for aspiring Academic Interns (AIs). It provides pedagogical training and guidance to students by introducing them to the Big Ideas of Teaching and Learning, and how to put them into practice. The course covers what makes a safe learning environment, how students learn, how to guide students toward mastery, and psychosocial factors that can negatively affect even the best students and best teachers. Class covers both theoretical and practical pedagogical aspects of teaching STEM subjects—specifically Computer Science. An integral feature of the course lies in the weekly AI experience that students perform to practice their teaching skills. Introduction to Instructional Methods in Computer Science for Academic Interns: Read More [+]

Prerequisites: Completion of any DS or CS lower-division course and concurrent participation in the Academic Intern experience in EECS at UC Berkeley

Fall and/or spring: 15 weeks - 2-2 hours of lecture and 3-9 hours of fieldwork per week

Summer: 8 weeks - 4-4 hours of lecture and 6-18 hours of fieldwork per week

Additional Format: Two hours of lecture and three to nine hours of fieldwork per week. Four hours of lecture and six to eightteen hours of fieldwork per week for 8 weeks.

Instructors: Hunn, Garcia

Introduction to Instructional Methods in Computer Science for Academic Interns: Read Less [-]

COMPSCI 370 Adaptive Instruction Methods in Computer Science 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This is a course for aspiring teachers or those who want to instruct with expertise from evidence-based research and proven equity-oriented practices. It provides pedagogical training by introducing the big ideas of teaching and learning, and illustrating how to put them into practice. The course is divided into three sections—instructing the individual; a group; and psycho-social factors that affect learning at any level. These sections are designed to enhance any intern’s, tutor’s, or TA’s teaching skillset. Class is discussion based, and covers theoretical and practical pedagogical aspects to teaching in STEM. An integral feature of the course involves providing weekly tutoring sessions. Adaptive Instruction Methods in Computer Science: Read More [+]

Prerequisites: Prerequisite satisfied Concurrently: experience tutoring or as an academic intern; or concurrently serving as an academic intern while taking course

Instructor: Hunn

Adaptive Instruction Methods in Computer Science: Read Less [-]

COMPSCI 375 Teaching Techniques for Computer Science 2 Units

Terms offered: Spring 2024, Spring 2023, Fall 2022 Discussion and practice of techniques for effective teaching, focusing on issues most relevant to teaching assistants in computer science courses. Teaching Techniques for Computer Science: Read More [+]

Prerequisites: Consent of instructor

Fall and/or spring: 15 weeks - 2 hours of discussion per week

Summer: 8 weeks - 4 hours of discussion per week

Additional Format: Two hours of discussion per week. Four hours of discussion per week for 8 weeks.

Instructors: Barsky, Garcia, Harvey

Teaching Techniques for Computer Science: Read Less [-]

COMPSCI 399 Professional Preparation: Supervised Teaching of Computer Science 1 or 2 Units

Terms offered: Spring 2020, Fall 2018, Fall 2016 Discussion, problem review and development, guidance of computer science laboratory sections, course development, supervised practice teaching. Professional Preparation: Supervised Teaching of Computer Science: Read More [+]

Prerequisites: Appointment as graduate student instructor

Fall and/or spring: 15 weeks - 1-2 hours of independent study per week

Summer: 8 weeks - 1-2 hours of independent study per week

Additional Format: One hour of meeting with instructor plus 10 hours (1 unit) or 20 hours(2 units) of teaching per week. One hour of meeting with instructor plus 20 hours (1 unit) or 40 hours (2 units) of teaching per week.

Professional Preparation: Supervised Teaching of Computer Science: Read Less [-]

COMPSCI 602 Individual Study for Doctoral Students 1 - 8 Units

Terms offered: Fall 2015, Fall 2014, Spring 2014 Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. (and other doctoral degrees). Individual Study for Doctoral Students: Read More [+]

Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree.

Summer: 8 weeks - 6-45 hours of independent study per week

Additional Format: Forty-five hours of work per unit per term. Independent study, consultation with faculty member.

Subject/Course Level: Computer Science/Graduate examination preparation

Individual Study for Doctoral Students: Read Less [-]

EL ENG 206A Introduction to Robotics 4 Units

Terms offered: Fall 2017, Fall 2016, Fall 2015 An introduction to the kinematics, dynamics, and control of robot manipulators, robotic vision, and sensing. The course will cover forward and inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics and control-position, and force control. Proximity, tactile, and force sensing. Network modeling, stability, and fidelity in teleoperation and medical applications of robotics. Introduction to Robotics: Read More [+]

Prerequisites: 120 or equivalent, or consent of instructor

Credit Restrictions: Students will receive no credit for 206A after taking C125/Bioengineering C125 or EE C106A

Additional Format: Three hours of Lecture, One hour of Discussion, and Three hours of Laboratory per week for 15 weeks.

Subject/Course Level: Electrical Engineering/Graduate

Instructor: Bajcsy

Formerly known as: Electrical Engineering 215A

EL ENG 206B Robotic Manipulation and Interaction 4 Units

Terms offered: Spring 2018, Spring 2017 This course is a sequel to EECS 125/225, which covers kinematics, dynamics and control of a single robot. This course will cover dynamics and control of groups of robotic manipulators coordinating with each other and interacting with the environment. Concepts will include an introduction to grasping and the constrained manipulation, contacts and force control for interaction with the environment. We will also cover active perception guided manipulation, as well as the manipulation of non-rigid objects. Throughout, we will emphasize design and human-robot interactions, and applications to applications in manufacturing, service robotics, tele-surgery, and locomotion. Robotic Manipulation and Interaction: Read More [+]

Course Objectives: To teach students the connection between the geometry, physics of manipulators with experimental setups that include sensors, control of large degrees of freedom manipulators, mobile robots and different grippers.

Student Learning Outcomes: By the end of the course students will be able to build a complete system composed of perceptual planning and autonomously controlled manipulators and /or mobile systems, justified by predictive theoretical models of performance.

Prerequisites: EL ENG 206A / BIO ENG C125 ; or consent of the instructor

Additional Format: Three hours of lecture and three hours of laboratory and one hour of discussion per week.

EL ENG 210 Applied Electromagnetic Theory 3 Units

Terms offered: Spring 2011, Spring 2010, Fall 2006 Advanced treatment of classical electromagnetic theory with engineering applications. Boundary value problems in electrostatics. Applications of Maxwell's Equations to the study of waveguides, resonant cavities, optical fiber guides, Gaussian optics, diffraction, scattering, and antennas. Applied Electromagnetic Theory: Read More [+]

Prerequisites: EL ENG 117 ; or PHYSICS 110A and PHYSICS 110B

Formerly known as: 210A-210B

Applied Electromagnetic Theory: Read Less [-]

EL ENG 213A Power Electronics 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Power conversion circuits and techniques. Characterization and design of magnetic devices including transformers, inductors, and electromagnetic actuators. Characteristics of power semiconductor devices, including power diodes, SCRs, MOSFETs, IGBTs, and emerging wide bandgap devices. Applications to renewable energy systems, high-efficiency lighting, power management in mobile electronics, and electric machine drives. Simulation based laboratory and design project. Power Electronics: Read More [+]

Prerequisites: EL ENG 105 or background in circuit analysis (KVL, KCL, voltage/current relationships, etc.)

Instructors: Pilawa, Boles

Power Electronics: Read Less [-]

EL ENG 213B Power Electronics Design 4 Units

Terms offered: Spring 2024 This course is the second in a two-semester series to equip students with the skills needed to analyze, design, and prototype power electronic converters. While EE 113/213A provides an overview of power electronics fundamentals and applications, EE 113B/213B focuses on the practical design and hardware implementation of power converters. The primary focus of EE 113B/213B is time in the laboratory, with sequential modules on topics such as power electronic components , PCB layout, closed-loop control, and experimental validation. At the end of the course, students will have designed, prototyped, and validated a power converter from scratch, demonstrating a skill set that is critical for power electronics engineers in research and industry. Power Electronics Design: Read More [+]

Repeat rules: Course may be repeated for credit with instructor consent.

Fall and/or spring: 15 weeks - 1.5 hours of lecture and 6 hours of laboratory per week

Additional Format: One and one-half hours of lecture and six hours of laboratory per week.

Instructor: Boles

Power Electronics Design: Read Less [-]

EL ENG C213 X-rays and Extreme Ultraviolet Radiation 3 Units

Terms offered: Spring 2022, Spring 2021, Fall 2019 This course explores modern developments in the physics and applications of x-rays and extreme ultraviolet (EUV) radiation. It begins with a review of electromagnetic radiation at short wavelengths including dipole radiation, scattering and refractive index, using a semi-classical atomic model. Subject matter includes the generation of x-rays with synchrotron radiation, high harmonic generation, x-ray free electron lasers, laser-plasma sources. Spatial and temporal coherence concepts are explained. Optics appropriate for this spectral region are described. Applications include nanoscale and astrophysical imaging, femtosecond and attosecond probing of electron dynamics in molecules and solids, EUV lithography, and materials characteristics. X-rays and Extreme Ultraviolet Radiation: Read More [+]

Prerequisites: Physics 110, 137, and Mathematics 53, 54 or equivalent

Instructor: Attwood

Also listed as: AST C210

X-rays and Extreme Ultraviolet Radiation: Read Less [-]

EL ENG 218A Introduction to Optical Engineering 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Fundamental principles of optical systems. Geometrical optics and aberration theory. Stops and apertures, prisms, and mirrors. Diffraction and interference. Optical materials and coatings. Radiometry and photometry. Basic optical devices and the human eye. The design of optical systems. Lasers, fiber optics, and holography. Introduction to Optical Engineering: Read More [+]

Prerequisites: MATH 53 ; EECS 16A and EECS 16B , or MATH 54

Credit Restrictions: Students will receive no credit for Electrical Engineering 218A after taking Electrical Engineering 118 or 119.

Instructors: Waller, Kante

Introduction to Optical Engineering: Read Less [-]

EL ENG 219B Logic Synthesis 4 Units

Terms offered: Spring 2016, Spring 2015, Spring 2011 The course covers the fundamental techniques for the design and analysis of digital circuits. The goal is to provide a detailed understanding of basic logic synthesis and analysis algorithms, and to enable students to apply this knowledge in the design of digital systems and EDA tools. The course will present combinational circuit optimization (two-level and multi-level synthesis), sequential circuit optimization (state encoding, retiming) , timing analysis, testing, and logic verification. Logic Synthesis: Read More [+]

Additional Format: Three hours of Lecture and One hour of Discussion per week for 15 weeks.

Logic Synthesis: Read Less [-]

EL ENG C220A Advanced Control Systems I 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Input-output and state space representation of linear continuous and discrete time dynamic systems. Controllability, observability, and stability. Modeling and identification. Design and analysis of single and multi-variable feedback control systems in transform and time domain. State observer. Feedforward/preview control. Application to engineering systems. Advanced Control Systems I: Read More [+]

Instructors: Borrelli, Horowitz, Tomizuka, Tomlin

Also listed as: MEC ENG C232

Advanced Control Systems I: Read Less [-]

EL ENG C220B Experiential Advanced Control Design I 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Experience-based learning in the design of SISO and MIMO feedback controllers for linear systems. The student will master skills needed to apply linear control design and analysis tools to classical and modern control problems. In particular, the participant will be exposed to and develop expertise in two key control design technologies: frequency-domain control synthesis and time-domain optimization-based approach. Experiential Advanced Control Design I: Read More [+]

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

Additional Format: Three hours of Lecture and Two hours of Laboratory per week for 15 weeks.

Also listed as: MEC ENG C231A

Experiential Advanced Control Design I: Read Less [-]

EL ENG C220C Experiential Advanced Control Design II 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Experience-based learning in design, analysis, & verification of automatic control for uncertain systems. The course emphasizes use of practical algorithms, including thorough computer implementation for representative problems. The student will master skills needed to apply advanced model-based control analysis, design, and estimation to a variety of industrial applications. First-principles analysis is provided to explain and support the algorithms & methods. The course emphasizes model-based state estimation, including the Kalman filter, and particle filter. Optimal feedback control of uncertain systems is also discussed (the linear quadratic Gaussian problem) as well as considerations of transforming continuous-time to discrete time. Experiential Advanced Control Design II: Read More [+]

Prerequisites: Undergraduate controls course (e.g. MECENG 132, ELENG 128) Recommended: MECENG C231A/ELENG C220B and either MECENG C232/ELENG C220A or ELENG 221A

Instructor: Mueller

Also listed as: MEC ENG C231B

Experiential Advanced Control Design II: Read Less [-]

EL ENG C220D Input/Output Methods for Compositional System Analysis 2 Units

Terms offered: Prior to 2007 Introduction to input/output concepts from control theory, systems as operators in signal spaces, passivity and small-gain theorems, dissipativity theory, integral quadratic constraints. Compositional stabilility and performance certification for interconnected systems from subsystems input/output properties. Case studies in multi-agent systems, biological networks, Internet congestion control, and adaptive control. Input/Output Methods for Compositional System Analysis: Read More [+]

Course Objectives: Standard computational tools for control synthesis and verification do not scale well to large-scale, networked systems in emerging applications. This course presents a compositional methodology suitable when the subsystems are amenable to analytical and computational methods but the interconnection, taken as a whole, is beyond the reach of these methods. The main idea is to break up the task of certifying desired stability and performance properties into subproblems of manageable size using input/output properties. Students learn about the fundamental theory, as well as relevant algorithms and applications in several domains.

Instructors: Arcak, Packard

Also listed as: MEC ENG C220D

Input/Output Methods for Compositional System Analysis: Read Less [-]

EL ENG 221A Linear System Theory 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Basic system concepts; state-space and I/O representation. Properties of linear systems. Controllability, observability, minimality, state and output-feedback. Stability. Observers. Characteristic polynomial. Nyquist test. Linear System Theory: Read More [+]

Prerequisites: EL ENG 120 ; and MATH 110 recommended

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of recitation per week

Additional Format: Three hours of Lecture and Two hours of Recitation per week for 15 weeks.

Linear System Theory: Read Less [-]

EL ENG 222 Nonlinear Systems--Analysis, Stability and Control 3 Units

Terms offered: Spring 2017, Spring 2016, Spring 2015 Basic graduate course in non-linear systems. Second Order systems. Numerical solution methods, the describing function method, linearization. Stability - direct and indirect methods of Lyapunov. Applications to the Lure problem - Popov, circle criterion. Input-Output stability. Additional topics include: bifurcations of dynamical systems, introduction to the "geometric" theory of control for nonlinear systems, passivity concepts and dissipative dynamical systems. Nonlinear Systems--Analysis, Stability and Control: Read More [+]

Prerequisites: EL ENG 221A (may be taken concurrently)

Nonlinear Systems--Analysis, Stability and Control: Read Less [-]

EL ENG C222 Nonlinear Systems 3 Units

Terms offered: Spring 2023, Spring 2022, Spring 2021 Basic graduate course in nonlinear systems. Nonlinear phenomena, planar systems, bifurcations, center manifolds, existence and uniqueness theorems. Lyapunov’s direct and indirect methods, Lyapunov-based feedback stabilization. Input-to-state and input-output stability, and dissipativity theory. Computation techniques for nonlinear system analysis and design. Feedback linearization and sliding mode control methods. Nonlinear Systems: Read More [+]

Prerequisites: MATH 54 (undergraduate level ordinary differential equations and linear algebra)

Instructors: Arcak, Tomlin, Kameshwar

Also listed as: MEC ENG C237

Nonlinear Systems: Read Less [-]

EL ENG 223 Stochastic Systems: Estimation and Control 3 Units

Terms offered: Spring 2024, Fall 2022, Spring 2021 Parameter and state estimation. System identification. Nonlinear filtering. Stochastic control. Adaptive control. Stochastic Systems: Estimation and Control: Read More [+]

Prerequisites: EL ENG 226A (which students are encouraged to take concurrently)

Stochastic Systems: Estimation and Control: Read Less [-]

EL ENG 224A Digital Communications 4 Units

Terms offered: Fall 2010, Fall 2009, Fall 2008 Introduction to the basic principles of the design and analysis of modern digital communication systems. Topics include source coding; channel coding; baseband and passband modulation techniques; receiver design; channel equalization; information theoretic techniques; block, convolutional, and trellis coding techniques; multiuser communications and spread spectrum; multi-carrier techniques and FDM; carrier and symbol synchronization. Applications to design of digital telephone modems, compact disks, and digital wireless communication systems are illustrated. The concepts are illustrated by a sequence of MATLAB exercises. Digital Communications: Read More [+]

Prerequisites: EL ENG 120 and EL ENG 126

Additional Format: Four hours of Lecture and One hour of Discussion per week for 15 weeks.

Formerly known as: 224

Digital Communications: Read Less [-]

EL ENG 224B Fundamentals of Wireless Communication 3 Units

Terms offered: Spring 2013, Spring 2012, Spring 2010 Introduction of the fundamentals of wireless communication. Modeling of the wireless multipath fading channel and its basic physical parameters. Coherent and noncoherent reception. Diversity techniques over time, frequency, and space. Spread spectrum communication. Multiple access and interference management in wireless networks. Frequency re-use, sectorization. Multiple access techniques: TDMA, CDMA, OFDM. Capacity of wireless channels. Opportunistic communication. Multiple antenna systems: spatial multiplexing, space-time codes. Examples from existing wireless standards. Fundamentals of Wireless Communication: Read More [+]

Prerequisites: EL ENG 121 and EL ENG 226A

Instructor: Tse

Fundamentals of Wireless Communication: Read Less [-]

EL ENG 225D Audio Signal Processing in Humans and Machines 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Introduction to relevant signal processing and basics of pattern recognition. Introduction to coding, synthesis, and recognition. Models of speech and music production and perception. Signal processing for speech analysis. Pitch perception and auditory spectral analysis with applications to speech and music. Vocoders and music synthesizers. Statistical speech recognition, including introduction to Hidden Markov Model and Neural Network approac hes. Audio Signal Processing in Humans and Machines: Read More [+]

Prerequisites: EL ENG 123 and STAT 200A ; or graduate standing and consent of instructor

Instructor: Morgan

Audio Signal Processing in Humans and Machines: Read Less [-]

EL ENG C225E Principles of Magnetic Resonance Imaging 4 Units

Terms offered: Spring 2023, Spring 2021, Spring 2020, Spring 2019 Fundamentals of MRI including signal-to-noise ratio, resolution, and contrast as dictated by physics, pulse sequences, and instrumentation. Image reconstruction via 2D FFT methods. Fast imaging reconstruction via convolution-back projection and gridding methods and FFTs. Hardware for modern MRI scanners including main field, gradient fields, RF coils, and shim supplies. Software for MRI including imaging methods such as 2D FT , RARE, SSFP, spiral and echo planar imaging methods. Principles of Magnetic Resonance Imaging: Read More [+]

Course Objectives: Graduate level understanding of physics, hardware, and systems engineering description of image formation, and image reconstruction in MRI. Experience in Imaging with different MR Imaging systems. This course should enable students to begin graduate level research at Berkeley (Neuroscience labs, EECS and Bioengineering), LBNL or at UCSF (Radiology and Bioengineering) at an advanced level and make research-level contribution

Prerequisites: EL ENG 120 or BIO ENG C165 / EL ENG C145B or consent of instructor

Credit Restrictions: Students will receive no credit for Bioengineering C265/El Engineering C225E after taking El Engineering 265.

Repeat rules: Course may be repeated for credit under special circumstances: Students can only receive credit for 1 of the 2 versions of the class,BioEc265 or EE c225e, not both

Instructors: Conolly, Vandsburger

Also listed as: BIO ENG C265/NUC ENG C235

Principles of Magnetic Resonance Imaging: Read Less [-]

EL ENG 226A Random Processes in Systems 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Probability, random variables and their convergence, random processes. Filtering of wide sense stationary processes, spectral density, Wiener and Kalman filters. Markov processes and Markov chains. Gaussian, birth and death, poisson and shot noise processes. Elementary queueing analysis. Detection of signals in Gaussian and shot noise, elementary parameter estimation. Random Processes in Systems: Read More [+]

Prerequisites: EL ENG 120 and STAT 200A

Instructor: Anantharam

Formerly known as: 226

Random Processes in Systems: Read Less [-]

EL ENG 226B Applications of Stochastic Process Theory 2 Units

Terms offered: Spring 2017, Spring 2013, Spring 1997 Advanced topics such as: Martingale theory, stochastic calculus, random fields, queueing networks, stochastic control. Applications of Stochastic Process Theory: Read More [+]

Prerequisites: EL ENG 226A

Instructors: Anantharam, Varaiya

Applications of Stochastic Process Theory: Read Less [-]

EL ENG 227BT Convex Optimization 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Convex optimization is a class of nonlinear optimization problems where the objective to be minimized, and the constraints, are both convex. The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experiments with the optimization software CVX, and a discussion section. Convex Optimization: Read More [+]

Prerequisites: MATH 54 and STAT 2

Instructors: El Ghaoui, Wainwright

Convex Optimization: Read Less [-]

EL ENG C227C Convex Optimization and Approximation 3 Units

Terms offered: Spring 2022, Spring 2021, Spring 2020, Spring 2019, Spring 2018, Spring 2017 Convex optimization as a systematic approximation tool for hard decision problems. Approximations of combinatorial optimization problems, of stochastic programming problems, of robust optimization problems (i.e., with optimization problems with unknown but bounded data), of optimal control problems. Quality estimates of the resulting approximation. Applications in robust engineering design, statistics , control, finance, data mining, operations research. Convex Optimization and Approximation: Read More [+]

Prerequisites: 227A or consent of instructor

Also listed as: IND ENG C227B

Convex Optimization and Approximation: Read Less [-]

EL ENG C227T Introduction to Convex Optimization 4 Units

Terms offered: Prior to 2007 The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experience. Introduction to Convex Optimization: Read More [+]

Additional Format: Three hours of lecture and two hours of laboratory and one hour of discussion per week.

Formerly known as: Electrical Engineering C227A/Industrial Engin and Oper Research C227A

Also listed as: IND ENG C227A

Introduction to Convex Optimization: Read Less [-]

EL ENG 228A High Speed Communications Networks 3 Units

Terms offered: Fall 2014, Spring 2014, Fall 2011 Descriptions, models, and approaches to the design and management of networks. Optical transmission and switching technologies are described and analyzed using deterministic, stochastic, and simulation models. FDDI, DQDB, SMDS, Frame Relay, ATM, networks, and SONET. Applications demanding high-speed communication. High Speed Communications Networks: Read More [+]

Prerequisites: EL ENG 122 ; and EL ENG 226A (may be taken concurrently)

High Speed Communications Networks: Read Less [-]

EL ENG 229A Information Theory and Coding 3 Units

Terms offered: Fall 2024, Fall 2022, Fall 2021 Fundamental bounds of Shannon theory and their application. Source and channel coding theorems. Galois field theory, algebraic error-correction codes. Private and public-key cryptographic systems. Information Theory and Coding: Read More [+]

Prerequisites: STAT 200A ; and EL ENG 226 recommended

Instructors: Anantharam, Tse

Formerly known as: 229

Information Theory and Coding: Read Less [-]

EL ENG 229B Error Control Coding 3 Units

Terms offered: Spring 2019, Spring 2016, Fall 2013 Error control codes are an integral part of most communication and recording systems where they are primarily used to provide resiliency to noise. In this course, we will cover the basics of error control coding for reliable digital transmission and storage. We will discuss the major classes of codes that are important in practice, including Reed Muller codes, cyclic codes, Reed Solomon codes, convolutional codes, concatenated codes, turbo codes, and low density parity check codes. The relevant background material from finite field and polynomial algebra will be developed as part of the course. Overview of topics: binary linear block codes; Reed Muller codes; Galois fields; linear block codes over a finite field; cyclic codes; BCH and Reed Solomon codes; convolutional codes and trellis based decoding, message passing decoding algorithms; trellis based soft decision decoding of block codes; turbo codes; low density parity check codes. Error Control Coding: Read More [+]

Prerequisites: 126 or equivalent (some familiarity with basic probability). Prior exposure to information theory not necessary

Instructor: Anatharam

Error Control Coding: Read Less [-]

EL ENG 230A Integrated-Circuit Devices 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Overview of electronic properties of semiconductors. Metal-semiconductor contacts, pn junctions, bipolar transistors, and MOS field-effect transistors. Properties that are significant to device operation for integrated circuits. Silicon device fabrication technology. Integrated-Circuit Devices: Read More [+]

Prerequisites: EECS 16A AND EECS 16B

Credit Restrictions: Students will receive no credit for EL ENG 230A after completing EL ENG 130 , EL ENG 230M, or EL ENG W230A . A deficient grade in EL ENG 230A may be removed by taking EL ENG W230A .

Formerly known as: Electrical Engineering 230M

Integrated-Circuit Devices: Read Less [-]

EL ENG 230B Solid State Devices 4 Units

Terms offered: Fall 2020, Spring 2019, Spring 2018 Physical principles and operational characteristics of semiconductor devices. Emphasis is on MOS field-effect transistors and their behaviors dictated by present and probable future technologies. Metal-oxide-semiconductor systems, short-channel and high field effects, device modeling, and impact on analog, digital circuits. Solid State Devices: Read More [+]

Prerequisites: EL ENG 130

Credit Restrictions: Students will receive no credit for EL ENG 230B after completing EL ENG 231, or EL ENG W230B . A deficient grade in EL ENG 230B may be removed by taking EL ENG W230B .

Instructors: Subramanian, King Liu, Salahuddin

Formerly known as: Electrical Engineering 231

Solid State Devices: Read Less [-]

EL ENG 230C Solid State Electronics 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2018 Crystal structure and symmetries. Energy-band theory. Cyclotron resonance. Tensor effective mass. Statistics of electronic state population. Recombination theory. Carrier transport theory. Interface properties. Optical processes and properties. Solid State Electronics: Read More [+]

Prerequisites: EL ENG 131; and PHYSICS 137B

Instructors: Bokor, Salahuddin

Formerly known as: Electrical Engineering 230

Solid State Electronics: Read Less [-]

EL ENG W230A Integrated-Circuit Devices 4 Units

Terms offered: Spring 2019, Spring 2018, Spring 2017 Overview of electronic properties of semiconductors. Metal-semiconductor contacts, pn junctions, bipolar transistors, and MOS field-effect transistors. Properties that are significant to device operation for integrated circuits. Silicon device fabrication technology. Integrated-Circuit Devices: Read More [+]

Prerequisites: MAS-IC students only

Credit Restrictions: Students will receive no credit for Electrical Engineering W230A after taking Electrical Engineering 130, Electrical Engineering W130 or Electrical Engineering 230A.

Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 1 hour of web-based discussion per week

Summer: 10 weeks - 4.5 hours of web-based lecture and 1.5 hours of web-based discussion per week

Additional Format: Three hours of Web-based lecture and One hour of Web-based discussion per week for 15 weeks. Four and one-half hours of Web-based lecture and One and one-half hours of Web-based discussion per week for 10 weeks.

Instructors: Javey, Subramanian, King Liu

Formerly known as: Electrical Engineering W130

EL ENG W230B Solid State Devices 4 Units

Terms offered: Fall 2015 Physical principles and operational characteristics of semiconductor devices. Emphasis is on MOS field-effect transistors and their behaviors dictated by present and probable future technologies. Metal-oxide-semiconductor systems, short-channel and high field effects, device modeling, and impact on analog, digital circuits. Solid State Devices: Read More [+]

Prerequisites: EL ENG W230A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W230B after taking EE 230B.

Formerly known as: Electrical Engineering W231

EL ENG 232 Lightwave Devices 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course is designed to give an introduction and overview of the fundamentals of optoelectronic devices. Topics such as optical gain and absorption spectra, quantization effects, strained quantum wells, optical waveguiding and coupling, and hetero p-n junction will be covered. This course will focus on basic physics and design principles of semiconductor diode lasers, light emitting diodes, photodetectors and integrated optics. Practical applications of the devices will be also discussed. Lightwave Devices: Read More [+]

Prerequisites: EL ENG 130 ; PHYSICS 137A ; and EL ENG 117 recommended

Instructor: Wu

Lightwave Devices: Read Less [-]

EL ENG 234A Fundamentals of Photovoltaic Devices 4 Units

Terms offered: Not yet offered This course is designed to give an introduction, and overview of, the fundamentals of photovoltaic devices. Students will learn how solar cells work, understand the concepts and models of solar cell device physics, and formulate and solve relevant physical problems related to photovoltaic devices. Monocrystalline, thin film and third generation solar cells will be discussed and analyzed. Light management and economic considerations in a solar cell system will also be covered. Fundamentals of Photovoltaic Devices: Read More [+]

Prerequisites: EECS 16A and EECS 16B , or Math 54 and Physics 7B, or equivalent

Instructor: Arias

Fundamentals of Photovoltaic Devices: Read Less [-]

EL ENG C235 Nanoscale Fabrication 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022, Spring 2016, Spring 2015, Spring 2013 This course discusses various top-down and bottom-up approaches to synthesizing and processing nanostructured materials. The topics include fundamentals of self assembly, nano-imprint lithography, electron beam lithography, nanowire and nanotube synthesis, quantum dot synthesis (strain patterned and colloidal), postsynthesis modification (oxidation, doping, diffusion, surface interactions, and etching techniques). In addition, techniques to bridging length scales such as heterogeneous integration will be discussed. We will discuss new electronic, optical, thermal, mechanical, and chemical properties brought forth by the very small sizes. Nanoscale Fabrication: Read More [+]

Instructor: Chang-Hasnain

Also listed as: NSE C203

Nanoscale Fabrication: Read Less [-]

EL ENG 236A Quantum and Optical Electronics 3 Units

Terms offered: Fall 2023, Fall 2022, Spring 2021 Interaction of radiation with atomic and semiconductor systems, density matrix treatment, semiclassical laser theory (Lamb's), laser resonators, specific laser systems, laser dynamics, Q-switching and mode-locking, noise in lasers and optical amplifiers. Nonlinear optics, phase-conjugation, electrooptics, acoustooptics and magnetooptics, coherent optics, stimulated Raman and Brillouin scattering. Quantum and Optical Electronics: Read More [+]

Prerequisites: EL ENG 117A and PHYSICS 137A

Quantum and Optical Electronics: Read Less [-]

EL ENG C239 Partially Ionized Plasmas 3 Units

Terms offered: Spring 2010, Spring 2009, Spring 2007 Introduction to partially ionized, chemically reactive plasmas, including collisional processes, diffusion, sources, sheaths, boundaries, and diagnostics. DC, RF, and microwave discharges. Applications to plasma-assisted materials processing and to plasma wall interactions. Partially Ionized Plasmas: Read More [+]

Prerequisites: An upper division course in electromagnetics or fluid dynamics

Additional Format: Forty-five hours of lecture per term.

Formerly known as: 239

Also listed as: AST C239

Partially Ionized Plasmas: Read Less [-]

EL ENG 240A Analog Integrated Circuits 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Single and multiple stage transistor amplifiers. Operational amplifiers. Feedback amplifiers, 2-port formulation, source, load, and feedback network loading. Frequency response of cascaded amplifiers, gain-bandwidth exchange, compensation, dominant pole techniques, root locus. Supply and temperature independent biasing and references. Selected applications of analog circuits such as analog-to-digital converters, switched capacitor filters, and comparators. Hardware laboratory and design project. Analog Integrated Circuits: Read More [+]

Prerequisites: EL ENG 105

Credit Restrictions: Students will receive no credit for EL ENG 240A after completing EL ENG 140 , or EL ENG W240A . A deficient grade in EL ENG 240A may be removed by taking EL ENG W240A .

Instructors: Sanders, Nguyen

Analog Integrated Circuits: Read Less [-]

EL ENG 240B Advanced Analog Integrated Circuits 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Analysis and optimized design of monolithic operational amplifiers and wide-band amplifiers; methods of achieving wide-band amplification, gain-bandwidth considerations; analysis of noise in integrated circuits and low noise design. Precision passive elements, analog switches, amplifiers and comparators, voltage reference in NMOS and CMOS circuits, Serial, successive-approximation, and parallel analog-to-digital converters. Switched-capacitor and CCD filters. Applications to codecs, modems. Advanced Analog Integrated Circuits: Read More [+]

Prerequisites: EL ENG 140 / EL ENG 240A

Credit Restrictions: Students will receive no credit for EL ENG 240B after completing EL ENG 240, or EL ENG W240B . A deficient grade in EL ENG 240B may be removed by taking EL ENG W240B .

Advanced Analog Integrated Circuits: Read Less [-]

EL ENG 240C Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits 3 Units

Terms offered: Fall 2024, Spring 2023, Fall 2019 Architectural and circuit level design and analysis of integrated analog-to-digital and digital-to-analog interfaces in CMOS and BiCMOS VLSI technology. Analog-digital converters, digital-analog converters, sample/hold amplifiers, continuous and switched-capacitor filters. RF integrated electronics including synthesizers, LNA's, and baseband processing. Low power mixed signal design. Data communications functions including clock recovery. CAD tools for analog design including simulation and synthesis. Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read More [+]

Prerequisites: EL ENG 140

Credit Restrictions: Students will receive no credit for EL ENG 240C after completing EL ENG 290Y , or EL ENG W240C . A deficient grade in EL ENG 240C may be removed by taking EL ENG W240C .

Instructor: Boser

Formerly known as: Electrical Engineering 247

Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read Less [-]

EL ENG W240A Analog Integrated Circuits 4 Units

Terms offered: Spring 2020, Spring 2019, Spring 2018 Single and multiple stage transistor amplifiers. Operational amplifiers. Feedback amplifiers, 2-port formulation, source, load, and feedback network loading. Frequency response of cascaded amplifiers, gain-bandwidth exchange, compensation, dominant pole techniques, root locus. Supply and temperature independent biasing and references. Selected applications of analog circuits such as analog-to-digital converters, switched capacitor filters , and comparators. Analog Integrated Circuits: Read More [+]

Credit Restrictions: Students will receive no credit for EE W240A after taking EE 140 or EE 240A.

Instructors: Alon, Sanders, Nguyen

EL ENG W240B Advanced Analog Integrated Circuits 3 Units

Terms offered: Spring 2020, Spring 2019, Fall 2015 Analysis and optimized design of monolithic operational amplifiers and wide-band amplifiers; methods of achieving wide-band amplification, gain-bandwidth considerations; analysis of noise in integrated circuits and low noise design. Precision passive elements, analog switches, amplifiers and comparators, voltage reference in NMOS and CMOS circuits, Serial, successive-approximation, and parallel analog-to-digital converts. Switched-capacitor and CCD filters. Applications to codecs, modems. Advanced Analog Integrated Circuits: Read More [+]

Prerequisites: EL ENG W240A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W240B after taking EE 240B.

Summer: 10 weeks - 4.5 hours of web-based lecture per week

Additional Format: Three hours of Web-based lecture per week for 15 weeks. Four and one-half hours of Web-based lecture per week for 10 weeks.

Formerly known as: Electrical Engineering W240

EL ENG W240C Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits 3 Units

Terms offered: Spring 2017, Spring 2016 Architectural and circuit level design and analysis of integrated analog-to-digital and digital-to-analog interfaces in modern CMOS and BiCMOS VLSI technology. Analog-digital converters, digital-analog converters, sample/hold amplifiers, continuous and switched-capacitor filters. Low power mixed signal design techniques. Data communications systems including interface circuity. CAD tools for analog design for simulation and synthesis. Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read More [+]

Credit Restrictions: Students will receive no credit for EE W240C after taking EE 240C.

Formerly known as: Electrical Engineering W247

EL ENG 241B Advanced Digital Integrated Circuits 3 Units

Terms offered: Spring 2021, Spring 2020, Spring 2019 Analysis and design of MOS and bipolar large-scale integrated circuits at the circuit level. Fabrication processes, device characteristics, parasitic effects static and dynamic digital circuits for logic and memory functions. Calculation of speed and power consumption from layout and fabrication parameters. ROM, RAM, EEPROM circuit design. Use of SPICE and other computer aids. Advanced Digital Integrated Circuits: Read More [+]

Prerequisites: EL ENG 141

Credit Restrictions: Students will receive no credit for EL ENG 241B after completing EL ENG 241, or EL ENG W241B . A deficient grade in EL ENG 241B may be removed by taking EL ENG W241B .

Instructors: Nikolic, Rabaey

Formerly known as: Electrical Engineering 241

Advanced Digital Integrated Circuits: Read Less [-]

EL ENG W241A Introduction to Digital Integrated Circuits 4 Units

Terms offered: Fall 2015, Fall 2014, Spring 2014 CMOS devices and deep sub-micron manufacturing technology. CMOS inverters and complex gates. Modeling of interconnect wires. Optimization of designs with respect to a number of metrics: cost, reliability, performance, and power dissipation. Sequential circuits, timing considerations, and clocking approaches. Design of large system blocks, including arithmetic, interconnect, memories, and programmable logic arrays. Introduction to design methodologies , including laboratory experience. Introduction to Digital Integrated Circuits: Read More [+]

Credit Restrictions: Students will receive no credit for W241A after taking EE 141 or EE 241A.

Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 4 hours of web-based discussion per week

Summer: 10 weeks - 4.5 hours of web-based lecture and 6 hours of web-based discussion per week

Additional Format: F/Sp: Three hours of web-based lecture, one hour of web-based discussion, and three hours of web-based laboratory per week. Su: Four and one-half hours of web-based lecture, one and one-half hours of web-based discussion, and four and one-half hours of web-based laboratory per week for ten weeks.

Instructors: Alon, Rabaey, Nikolic

Introduction to Digital Integrated Circuits: Read Less [-]

EL ENG W241B Advanced Digital Integrated Circuits 3 Units

Terms offered: Spring 2017, Spring 2016, Spring 2015 Analysis and design of MOS and bipolar large-scale integrated circuits at the circuit level. Fabrication processes, device characteristics, parasitic effects static and dynamic digital circuits for logic and memory functions. Calculation of speed and power consumption from layout and fabrication parameters. ROM, RAM, EEPROM circuit design. Use of SPICE and other computer aids. Advanced Digital Integrated Circuits: Read More [+]

Prerequisites: EL ENG W241A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W241B after taking EE 241B.

Formerly known as: Electrical Engineering W241

EL ENG 242A Integrated Circuits for Communications 4 Units

Terms offered: Fall 2023, Spring 2023, Spring 2022 Analysis and design of electronic circuits for communication systems, with an emphasis on integrated circuits for wireless communication systems. Analysis of noise and distortion in amplifiers with application to radio receiver design. Power amplifier design with application to wireless radio transmitters. Radio-frequency mixers, oscillators, phase-locked loops, modulators, and demodulators. Integrated Circuits for Communications: Read More [+]

Prerequisites: EL ENG 140 /240A or equivalent

Credit Restrictions: Students will receive no credit for Electrical Engineering 242A after taking Electrical Engineering 142.

Formerly known as: Electrical Engineering 242M

Integrated Circuits for Communications: Read Less [-]

EL ENG 242B Advanced Integrated Circuits for Communications 3 Units

Terms offered: Fall 2024, Fall 2020, Fall 2014 Analysis, evaluation and design of present-day integrated circuits for communications application, particularly those for which nonlinear response must be included. MOS, bipolar and BICMOS circuits, audio and video power amplifiers, optimum performance of near-sinusoidal oscillators and frequency-translation circuits. Phase-locked loop ICs, analog multipliers and voltage-controlled oscillators; advanced components for telecommunication circuits. Use of new CAD tools and systems. Advanced Integrated Circuits for Communications: Read More [+]

Prerequisites: EL ENG 142 and EL ENG 240

Credit Restrictions: Students will receive no credit for EL ENG 242B after completing EL ENG 242, or EL ENG W242B . A deficient grade in EL ENG 242B may be removed by taking EL ENG W242B .

Instructor: Niknejad

Formerly known as: Electrical Engineering 242

Advanced Integrated Circuits for Communications: Read Less [-]

EL ENG W242A Integrated Circuits for Communications 4 Units

Terms offered: Spring 2020, Spring 2019, Spring 2018 Analysis and design of electronic circuits for communication systems, with an emphasis on integrated circuits for wireless communication systems. Analysis of noise and distortion in amplifiers with application to radio receiver design. Power amplifier design with application to wireless radio transmitters. Radio-frequency mixers, oscillators, phase-locked loops, modulators, and demodulators. Integrated Circuits for Communications: Read More [+]

Credit Restrictions: Students will receive no credit for EE W242A after taking EE 142, EE 242A, or EE 242B.

Formerly known as: Electrical Engineering W142

EL ENG W242B Advanced Integrated Circuits for Communications 3 Units

Terms offered: Spring 2017, Spring 2016 Analysis, evaluation, and design of present-day integrated circuits for communications application, particularly those for which nonlinear response must be included. MOS, bipolar and BICMOS circuits, audio and video power amplifiers, optimum performance of near-sinusoidal oscillators and frequency-translation circuits. Phase-locked loop ICs, analog multipliers and voltage-controlled oscillators; advanced components for telecommunication circuits. Use of new CAD tools and systems. Advanced Integrated Circuits for Communications: Read More [+]

Prerequisites: EL ENG W240A ; EL ENG W242A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W242B after taking EE 242B.

Formerly known as: Electrical Engineering W242

EL ENG 243 Advanced IC Processing and Layout 3 Units

Terms offered: Spring 2014, Spring 2012, Spring 2011 The key processes for the fabrication of integrated circuits. Optical, X-ray, and e-beam lithography, ion implantation, oxidation and diffusion. Thin film deposition. Wet and dry etching and ion milling. Effect of phase and defect equilibria on process control. Advanced IC Processing and Layout: Read More [+]

Prerequisites: EL ENG 143 ; and either EL ENG 140 or EL ENG 141

Advanced IC Processing and Layout: Read Less [-]

EL ENG 244 Fundamental Algorithms for Systems Modeling, Analysis, and Optimization 4 Units

Terms offered: Fall 2016, Fall 2015, Fall 2014 The modeling, analysis, and optimization of complex systems requires a range of algorithms and design software. This course reviews the fundamental techniques underlying the design methodology for complex systems, using integrated circuit design as example. Topics include design flows, discrete and continuous models and algorithms, and strategies for implementing algorithms efficiently and correctly in software. Laboratory assignments and a class project will expose students to state-of-the-art. Fundamental Algorithms for Systems Modeling, Analysis, and Optimization: Read More [+]

Prerequisites: Graduate standing

Credit Restrictions: Students will receive no credit for EL ENG 244 after completing EL ENG W244 .

Instructors: Keutzer, Lee, Roychowdhury, Seshia

Fundamental Algorithms for Systems Modeling, Analysis, and Optimization: Read Less [-]

EL ENG W244 Fundamental Algorithms for System Modeling, Analysis, and Optimization 4 Units

Terms offered: Fall 2015 The modeling, analysis, and optimization of complex systems require a range of algorithms and design tools. This course reviews the fundamental techniques underlying the design methodology for complex systems, using integrated circuit design as an example. Topics include design flows, discrete and continuous models and algorithms, and strategies for implementing algorithms efficiently and correctly in software. Fundamental Algorithms for System Modeling, Analysis, and Optimization: Read More [+]

Credit Restrictions: Students will receive no credit for W244 after taking 144 and 244.

Fundamental Algorithms for System Modeling, Analysis, and Optimization: Read Less [-]

EL ENG C246 Parametric and Optimal Design of MEMS 3 Units

Terms offered: Spring 2013, Spring 2012, Spring 2011 Parametric design and optimal design of MEMS. Emphasis on design, not fabrication. Analytic solution of MEMS design problems to determine the dimensions of MEMS structures for specified function. Trade-off of various performance requirements despite conflicting design requirements. Structures include flexure systems, accelerometers, and rate sensors. Parametric and Optimal Design of MEMS: Read More [+]

Prerequisites: Graduate standing or consent of instructor

Instructors: Lin, Pisano

Formerly known as: 219

Also listed as: MEC ENG C219

Parametric and Optimal Design of MEMS: Read Less [-]

EL ENG 247A Introduction to Microelectromechanical Systems (MEMS) 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course will teach fundamentals of micromachining and microfabrication techniques, including planar thin-film process technologies, photolithographic techniques, deposition and etching techniques, and the other technologies that are central to MEMS fabrication. It will pay special attention to teaching of fundamentals necessary for the design and analysis of devices and systems in mechanical, electrical, fluidic, and thermal energy/signal domains , and will teach basic techniques for multi-domain analysis. Fundamentals of sensing and transduction mechanisms including capacitive and piezoresistive techniques, and design and analysis of micmicromachined miniature sensors and actuators using these techniques will be covered. Introduction to Microelectromechanical Systems (MEMS): Read More [+]

Prerequisites: EECS 16A and EECS 16B ; or consent of instructor required

Credit Restrictions: Students will receive no credit for EE 247A after taking EE 147.

Instructors: Maharbiz, Nguyen, Pister

Introduction to Microelectromechanical Systems (MEMS): Read Less [-]

EL ENG C247B Introduction to MEMS Design 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022, Spring 2021, Spring 2020 Physics, fabrication, and design of micro-electromechanical systems (MEMS). Micro and nanofabrication processes, including silicon surface and bulk micromachining and non-silicon micromachining. Integration strategies and assembly processes. Microsensor and microactuator devices: electrostatic, piezoresistive, piezoelectric, thermal, magnetic transduction. Electronic position-sensing circuits and electrical and mechanical noise. CAD for MEMS. Design project is required. Introduction to MEMS Design: Read More [+]

Prerequisites: Graduate standing in engineering or science; undergraduates with consent of instructor

Instructors: Nguyen, Pister

Formerly known as: Electrical Engineering C245, Mechanical Engineering C218

Also listed as: MEC ENG C218

Introduction to MEMS Design: Read Less [-]

EL ENG W247B Introduction to MEMS Design 4 Units

Terms offered: Prior to 2007 Physics, fabrication and design of micro electromechanical systems (MEMS). Micro and nano-fabrication processes, including silicon surface and bulk micromachining and non-silicon micromachining. Integration strategies and assembly processes. Microsensor and microactuator devices: electrostatic, piezoresistive, piezoelectric, thermal, and magnetic transduction. Electronic position-sensing circuits and electrical and mechanical noise. CAD for MEMS. Design project is required. Introduction to MEMS Design: Read More [+]

Credit Restrictions: Students will receive no credit for EE W247B after taking EE C247B or Mechanical Engineering C218.

Formerly known as: Electrical Engineering W245

EL ENG 248C Numerical Modeling and Analysis: Nonlinear Systems and Noise 4 Units

Terms offered: Prior to 2007 Numerical modelling and analysis techniques are widely used in scientific and engineering practice; they are also an excellent vehicle for understanding and concretizing theory. This course covers topics important for a proper understanding of nonlinearity and noise: periodic steady state and envelope ("RF") analyses; oscillatory systems; nonstationary and phase noise; and homotopy/continuation techniques for solving "difficult" equation systems. An underlying theme of the course is relevance to different physical domains, from electronics (e.g., analog/RF/mixed-signal circuits, high-speed digital circuits, interconnect, etc.) to optics, nanotechnology, chemistry, biology and mechanics. Hands-on coding using the MATLAB-based Berkeley Model Numerical Modeling and Analysis: Nonlinear Systems and Noise: Read More [+]

Course Objectives: Homotopy techniques for robust nonlinear equation solution Modelling and analysis of oscillatory systems - harmonic, ring and relaxation oscillators - oscillator steady state analysis - perturbation analysis of amplitude-stable oscillators RF (nonlinear periodic steady state) analysis - harmonic balance and shooting - Multi-time PDE and envelope methods - perturbation analysis of periodic systems (Floquet theory) RF (nonlinear, nonstationary) noise concepts and their application - cyclostationary noise analysis - concepts of phase noise in oscillators Using MAPP for fast/convenient modelling and analysis

Student Learning Outcomes: Students will develop a facility in the above topics and be able to apply them widely across science and engineering.

Prerequisites: Consent of Instructor

Numerical Modeling and Analysis: Nonlinear Systems and Noise: Read Less [-]

EL ENG C249A Introduction to Embedded Systems 4 Units

Also listed as: COMPSCI C249A

EL ENG C261 Medical Imaging Signals and Systems 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Biomedical imaging is a clinically important application of engineering, applied mathematics, physics, and medicine. In this course, we apply linear systems theory and basic physics to analyze X-ray imaging, computerized tomography, nuclear medicine, and MRI. We cover the basic physics and instrumentation that characterizes medical image as an ideal perfect-resolution image blurred by an impulse response. This material could prepare the student for a career in designing new medical imaging systems that reliably detect small tumors or infarcts. Medical Imaging Signals and Systems: Read More [+]

Course Objectives: • understand how 2D impulse response or 2D spatial frequency transfer function (or Modulation Transfer Function) allow one to quantify the spatial resolution of an imaging system. • understand 2D sampling requirements to avoid aliasing • understand 2D filtered backprojection reconstruction from projections based on the projection-slice theorem of Fourier Transforms • understand the concept of image reconstruction as solving a mathematical inverse problem. • understand the limitations of poorly conditioned inverse problems and noise amplification • understand how diffraction can limit resolution---but not for the imaging systems in this class • understand the hardware components of an X-ray imaging scanner • • understand the physics and hardware limits to spatial resolution of an X-ray imaging system • understand tradeoffs between depth, contrast, and dose for X-ray sources • understand resolution limits for CT scanners • understand how to reconstruct a 2D CT image from projection data using the filtered backprojection algorithm • understand the hardware and physics of Nuclear Medicine scanners • understand how PET and SPECT images are created using filtered backprojection • understand resolution limits of nuclear medicine scanners • understand MRI hardware components, resolution limits and image reconstruction via a 2D FFT • understand how to construct a medical imaging scanner that will achieve a desired spatial resolution specification.

Student Learning Outcomes: • students will be tested for their understanding of the key concepts above • undergraduate students will apply to graduate programs and be admitted • students will apply this knowledge to their research at Berkeley, UCSF, the national labs or elsewhere • students will be hired by companies that create, sell, operate or consult in biomedical imaging

Prerequisites: Undergraduate level course work covering integral and differential calculus, two classes in engineering-level physics, introductory level linear algebra, introductory level statistics, at least 1 course in LTI system theory including (analog convolution, Fourier transforms, and Nyquist sampling theory). The recommended undergrad course prerequisites are introductory level skills in Python or Matlab and either EECS 16A , EECS 16B and EL ENG 120 , or MATH 54 , BIO ENG 101 , and BIO ENG 105

Instructor: Conolly

Also listed as: BIO ENG C261/NUC ENG C231

Medical Imaging Signals and Systems: Read Less [-]

EL ENG 290 Advanced Topics in Electrical Engineering 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Read More [+]

Repeat rules: Course may be repeated for credit when topic changes.

Additional Format: One to three hours of lecture per week. Two to five hours of lecture per week for 10 weeks. Two to six hours of lecture per week for 8 weeks. Three to nine hours of lecture per week for 6 weeks. Three to fifteen hours of lecture per week for four weeks.

Advanced Topics in Electrical Engineering: Read Less [-]

EL ENG 290A Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design 1 - 3 Units

Terms offered: Spring 2016, Spring 2015, Fall 2014 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design: Read More [+]

Fall and/or spring: 15 weeks - 1-3 hours of lecture per week

Additional Format: One to Three hour of Lecture per week for 15 weeks.

Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design: Read Less [-]

EL ENG 290B Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices 1 - 3 Units

Terms offered: Spring 2021, Spring 2020, Spring 2019 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices: Read Less [-]

EL ENG 290C Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design 1 - 3 Units

Terms offered: Spring 2019, Fall 2018, Spring 2018 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design: Read Less [-]

EL ENG 290D Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology 1 - 3 Units

Terms offered: Spring 2021, Fall 2014, Fall 2013 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology: Read Less [-]

EL ENG 290F Advanced Topics in Electrical Engineering: Advanced Topics in Photonics 1 - 3 Units

Terms offered: Spring 2014, Fall 2013, Fall 2012 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Photonics: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Photonics: Read Less [-]

EL ENG 290G Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators 1 - 3 Units

Terms offered: Fall 2017, Fall 2016, Spring 2002 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators: Read More [+]

Formerly known as: Engineering 210

Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators: Read Less [-]

EL ENG 290N Advanced Topics in Electrical Engineering: Advanced Topics in System Theory 1 - 3 Units

Terms offered: Fall 2018, Fall 2017, Fall 2015 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in System Theory: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in System Theory: Read Less [-]

EL ENG 290O Advanced Topics in Electrical Engineering: Advanced Topics in Control 1 - 3 Units

Terms offered: Spring 2019, Fall 2018, Fall 2017 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Control: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Control: Read Less [-]

EL ENG 290P Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics 1 - 3 Units

Terms offered: Spring 2019, Spring 2018, Fall 2017 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics: Read Less [-]

EL ENG 290Q Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks 1 - 3 Units

Terms offered: Spring 2017, Spring 2016, Fall 2014 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks: Read Less [-]

EL ENG 290S Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory 1 - 3 Units

Terms offered: Fall 2018, Fall 2016, Fall 2009 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory: Read Less [-]

EL ENG 290T Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing 1 - 3 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing: Read Less [-]

EL ENG 290Y Advanced Topics in Electrical Engineering: Organic Materials in Electronics 3 Units

Terms offered: Spring 2014, Spring 2013, Fall 2009 Organic materials are seeing increasing application in electronics applications. This course will provide an overview of the properties of the major classes of organic materials with relevance to electronics. Students will study the technology, physics, and chemistry of their use in the three most rapidly growing major applications--energy conversion/generation devices (fuel cells and photovoltaics), organic light-emitting diodes, and organic transistors. Advanced Topics in Electrical Engineering: Organic Materials in Electronics: Read More [+]

Prerequisites: EL ENG 130 ; and undergraduate general chemistry

Instructor: Subramanian

Advanced Topics in Electrical Engineering: Organic Materials in Electronics: Read Less [-]

EL ENG W290C Advanced Topics in Circuit Design 3 Units

Terms offered: Prior to 2007 Seminar-style course presenting an in-depth perspective on one specific domain of integrated circuit design. Most often, this will address an application space that has become particularly relevant in recent times. Examples are serial links, ultra low-power design, wireless transceiver design, etc. Advanced Topics in Circuit Design: Read More [+]

Credit Restrictions: Students will receive no credit for W290C after taking 290C.

Advanced Topics in Circuit Design: Read Less [-]

EL ENG C291 Control and Optimization of Distributed Parameters Systems 3 Units

Terms offered: Fall 2017, Spring 2016, Spring 2015, Spring 2014 Distributed systems and PDE models of physical phenomena (propagation of waves, network traffic, water distribution, fluid mechanics, electromagnetism, blood vessels, beams, road pavement, structures, etc.). Fundamental solution methods for PDEs: separation of variables, self-similar solutions, characteristics, numerical methods, spectral methods. Stability analysis. Adjoint-based optimization. Lyapunov stabilization. Differential flatness. Viability control. Hamilton-Jacobi-based control. Control and Optimization of Distributed Parameters Systems: Read More [+]

Prerequisites: ENGIN 7 and MATH 54 ; or consent of instructor

Also listed as: CIV ENG C291F/MEC ENG C236

Control and Optimization of Distributed Parameters Systems: Read Less [-]

EL ENG C291E Hybrid Systems and Intelligent Control 3 Units

Terms offered: Spring 2021, Spring 2020, Spring 2018 Analysis of hybrid systems formed by the interaction of continuous time dynamics and discrete-event controllers. Discrete-event systems models and language descriptions. Finite-state machines and automata. Model verification and control of hybrid systems. Signal-to-symbol conversion and logic controllers. Adaptive, neural, and fuzzy-control systems. Applications to robotics and Intelligent Vehicle and Highway Systems (IVHS). Hybrid Systems and Intelligent Control: Read More [+]

Formerly known as: 291E

Also listed as: MEC ENG C290S

Hybrid Systems and Intelligent Control: Read Less [-]

EL ENG 297 Field Studies in Electrical Engineering 12 Units

Terms offered: Summer 2024 8 Week Session, Fall 2023, Summer 2023 8 Week Session Supervised experience in off-campus companies relevant to specific aspects and applications of electrical engineering. Written report required at the end of the semester. Field Studies in Electrical Engineering: Read More [+]

Summer: 8 weeks - 1-12 hours of independent study per week

Additional Format: Individual conferences. Individual conferences.

Field Studies in Electrical Engineering: Read Less [-]

EL ENG 298 Group Studies, Seminars, or Group Research 1 - 4 Units

Terms offered: Spring 2023, Spring 2022, Spring 2021 Advanced study in various subjects through special seminars on topics to be selected each year, informal group studies of special problems, group participation in comprehensive design problems, or group research on complete problems for analysis and experimentation. Group Studies, Seminars, or Group Research: Read More [+]

Fall and/or spring: 15 weeks - 0 hours of lecture per week

Additional Format: One to four hours of lectures per unit.

Group Studies, Seminars, or Group Research: Read Less [-]

EL ENG 299 Individual Research 1 - 12 Units

Terms offered: Summer 2024 10 Week Session, Summer 2023 10 Week Session, Spring 2023 Investigation of problems in electrical engineering. Individual Research: Read More [+]

Summer: 6 weeks - 2.5-30 hours of independent study per week 8 weeks - 1.5-22.5 hours of independent study per week

Additional Format: Independent, individual study or investigation. Independent, individual study or investigation. Forty-five hours of work per unit per term.

EL ENG 375 Teaching Techniques for Electrical Engineering 2 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Discussion of effective teaching techniques. Use of educational objectives, alternative forms of instruction, and proven techniques to enhance student learning. This course is intended to orient new student instructors to more effectively teach courses offered by the Department of Electrical Engineering and Computer Sciences at UC Berkeley. Teaching Techniques for Electrical Engineering: Read More [+]

Prerequisites: Teaching assistant or graduate student

Fall and/or spring: 15 weeks - 1.5 hours of seminar per week

Additional Format: One and one-half hours of seminar per week.

Subject/Course Level: Electrical Engineering/Professional course for teachers or prospective teachers

Teaching Techniques for Electrical Engineering: Read Less [-]

EL ENG 602 Individual Study for Doctoral Students 1 - 8 Units

Terms offered: Fall 2016, Fall 2015, Fall 2014 Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. (and other doctoral degrees). Individual Study for Doctoral Students: Read More [+]

Additional Format: Forty-five hours of work per unit per term. Independent study, in consultation with faculty member.

Subject/Course Level: Electrical Engineering/Graduate examination preparation

Contact Information

Department of electrical engineering and computer sciences.

387 Soda Hall

Phone: 510-642-1042

Fax: 510-642-5775

Vice Chair, Graduate Study and Prelims

Ana Claudia Arias, PhD

508 Cory Hall

[email protected]

John Wawrzynek, PhD

631 Soda Hall

[email protected]

Vice Chair, Masters’ Degree Programs (MEng & MS)

Murat Arcak, PhD

569 Cory Hall

[email protected]

EECS Department Chair

Claire Tomlin, PhD

231 Cory Hall

Phone: 510-642-0253

[email protected]

EECS Associate Chair/CS Division Chair

David Wagner, PhD

389 Soda Hall

Phone: 510-642-7699

[email protected]

Executive Director, EECS Student Affairs

Susanne Kauer

221 Cory Hall

[email protected]

Director of Grad Matters, EE Grad Advisor

Shirley Salanio

217 Cory Hall

Phone: 510-643-8347

[email protected]

CS Graduate Student Advisor

Jean Nguyen

367 Soda Hall

Phone: 510-642-9413

[email protected]

Masters' Student Advisor

Michael Sun

215 Cory Hall

[email protected]

CS Graduate Admissions and GSI Recruitment

Glenna Anton

Phone: 510-642-6285

[email protected]

Graduate Admissions and EE GSI Recruitment

Phone: 510-642-9265

[email protected]

Graduate Student Advisor

Tiffany Grimsley

253 Cory Hall

[email protected]

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Carnegie Mellon University School of Computer Science

Doctoral programs.

In any of the Ph.D. programs across our seven departments, you'll be matched with an advisor based primarily on mutual research interests and begin a research project on day one. All our Ph.D. students receive full financial support while in good academic standing, which helps ensure freedom to explore regardless of funding hurdles. We also believe that it's vital for advisors and students to work as peers, and the inherent flexibility of our programs means students often work with more than one faculty member and many other students during their time in SCS.

Together, our research environment and interdisciplinary mindset produce graduates who emerge into the world ready to tackle its biggest problems.

• Doctoral Programs Home

Interested in Applying?

• Graduate Admissions Overview
• Frequently Asked Questions

Program Contact

Robert Frederking Associate Dean for Doctoral Programs

Explore Our Ph.D. Programs

Ray and stephanie lane computational biology department, computer science department, human-computer interaction institute.

Ph.D. in Human-Computer Interaction

Language Technologies Institute

Ph.D. in Language and Information Technologies

Machine Learning Department

Robotics institute.

Ph.D. in Robotics

Software and Societal Systems Department

Ph.D. in Societal Computing (SC) Ph.D. in Software Engineering (SE)

Dual Degree Ph.D. Programs

The carnegie mellon portugal program (cmu portugal), ph.d. in computer science/dual degree portugal, ph.d. in human-computer interaction/dual degree portugal, ph.d. in language and information technologies/dual degree portugal, ph.d. in robotics/dual degree portugal, ph.d. in software engineering/dual degree portugal.

Computer Science, PhD

Whiting school of engineering.

The goal of the Doctor of Philosophy (Ph.D.) program in the Department of Computer Science is to prepare first-rate scholars in computer science. Successful graduates may assume significant positions in academia, research institutes, industry, or government laboratories.

Applications for admission to the Ph.D. program in Computer Science are reviewed by a faculty committee. Although specific criteria isn’t rigid, all students admitted must exhibit exceptional intellectual achievements and promise. Applicants must submit letters of recommendation, and (for international applicants) TOEFL/IELTS scores. Visit https://engineering.jhu.edu/graduate-admissions/ for more information on the application process.

For details regarding CS Ph.D. program requirements and policies, please visit the Advising Manual on our departmental website.

Financial Aid

All full-time CS Ph.D. students are fully-funded for the duration of their Ph.D. career while in a fulltime, resident status- either in the form of a Research Assistantship directed by members of the faculty, a Teaching Assistantship (at least one semester of TA is required), or a fellowship.  Support includes full tuition and annual health insurance coverage, as well as a monthly living-stipend during the fall and spring academic semesters (9 months).  Students who wish to continue working with their advisor and remain researching/working towards their degree full-time with the University during the summer months will continue to receive their stipend for June, July, and August (as opposed to doing an external internship, etc.).

Program Requirements

University residency.

Two consecutive semesters of residence as a full-time graduate student are required.

Seminar Attendance

All Ph.D. degree candidates are required to maintain satisfactory attendance in the Computer Science Seminar each semester for the duration of their enrollment in the program.  Although seminar attendance is required, the seminar may not be counted toward the qualifying course requirement. Enrollment in the Computer Science Seminar EN.601.801 is required for first and second year students only.

Responsible Conduct of Research and Academic Ethics

All doctoral students are required to take AS.360.625 Responsible Conduct of Research . Students are expected to complete the course by the end of their first year. Failure to do so may result in a loss of funding. Additional information regarding this requirement can be found here: https://engineering.jhu.edu/research/resources-policies-forms/responsible-conduct-of-research-training-for-students-and-postdoctoral-fellows-revised-spring-2020/ . In addition, all doctoral students must complete the course EN.500.603 Graduate Orientation and Academic Ethics .

Qualifying Course Requirements

The Department of Computer Science classifies its courses into five core distribution areas: Applications, Reasoning, Software, Systems and Theory.  Ph.D. candidates must complete eight courses total (3 class hours/credits each), and at least five of those eight courses must be taught in the Department of Computer Science.  Of those courses, four out of the five core distribution areas must be satisfied.  A current  l isting of courses with area designators   is provided on the departmental website. The areas are also encoded as POS (program of study) tags in SIS. Ph.D. students may complete remaining elective graduate courses (chosen from any CS area or from closely related departments such as Electrical and Computer Engineering, Cognitive Science, Mathematics, or Applied Mathematics and Statistics) for a total of eight courses. Computer Science graduate students may count 600-level and above graduate courses. The coursework program must be approved by the student’s faculty advisor. The overall grade point average for these eight courses must be at least equivalent to a B+. No course with a grade of less than C- may be counted toward this Ph.D. qualifying course requirement. Other than independent study courses, no courses with grades of P or S can be counted toward the coursework requirement. Courses with grades of P or S will not be included in the grade point average calculation. One of the courses required for the degree, but only one, may be replaced by 3 credits from comparable short courses. With approval of the student’s faculty advisor, up to two courses can be transferred from graduate programs of other institutions; more than two such courses can be transferred with approval of the department. It is the obligation of the student to provide all necessary documentation to the Department of Computer Science regarding the course(s) for which transfer credit is being requested. Students are expected to complete the course requirements by the end of their second year as a Ph.D. candidate. 

Qualifying Project Requirements

A Ph.D. student must complete two projects, each under the supervision and written agreement of a different faculty member. One project must be under the supervision of a faculty member with an appointment in the Department of Computer Science (Professor, Research Professor, Visiting or Joint appointment). The second project can be supervised by a different tenure-track or research faculty member in any division of Johns Hopkins, or with advance approval from the department, by any outside researcher.  Upon conclusion of each project, the student must write a “Project Report” describing the project in detail. This report will be a public document and will be kept on file in the department office. The supervising faculty member must approve the project report. Students are expected to complete the qualifying projects by the end of their third year as a Ph.D. candidate. 

Upon completion of the Ph.D. qualifying course requirements and the first qualifying project, students are ordinarily eligible to receive a master of science in engineering degree. The degree will be awarded upon student request.

Graduate Board Oral Examination (GBO)

This examination is a university requirement, ideally taken in the student's third year. The oral exam is administered by a committee consisting 5 members.  Students must select two members from inside the department and two members from outside the department, plus a 5th member who is either inside or outside the department.  The exam seeks to establish the student’s readiness to conduct original research in the area of their “Preliminary Research Proposal,” which should be distributed to the examiners in advance and presented by the student at the start of the exam.

Part-Time Ph.D.

Two consecutive semesters of residence as a full-time graduate student are required by the university. Attempting to obtain a Ph.D. is a major commitment and involves close coordination with a faculty advisor in the department. Part-time students must be able to establish and maintain these close links, therefore part-time study is by advanced and special permission only.

Departmental Seminar

Ph.D. students must give an official departmental seminar on their research area. This is to be done after the GBO and prior to the dissertation defense, or as part of the dissertation defense.

Dissertation and Defense

Ph.D. students must write a dissertation consisting of original research in their chosen area. They must deliver a public presentation of the dissertation before a dissertation committee consisting of the faculty advisor, a second faculty member in the Department of Computer Science (who must have a primary tenure-track appointment in the Department if the advisor does not), and one or more other members with Ph.D. degrees. In conformity with University requirements, the members of the dissertation committee must submit a referee’s letter to the Graduate Board recommending that the dissertation be accepted. Completed dissertations will be formatted and submitted to the Milton S. Eisenhower Library for electronic publication .

Teaching Requirement

All Ph.D. students are required to serve as a Teaching Assistant at least one semester during their program of study. As part of the requirement, the supervising course instructor must give the TA an opportunity to be in front of a group of students at least once during the course. Students are required to sign-up for the course EN.601.807 Teaching Practicum  during the semester in which the requirement is being fulfilled, and at the end of the semester their performance will be evaluated by the course instructor.

Student Progress Review

Ph.D. students are reviewed annually by their advisor(s) and the department, and notified in writing as to their standing in the program. Students deemed to not be making satisfactory progress may be placed on probation.

PhD Program

The Department of Computer Science offers a Doctor of Philosophy (Ph.D.) in Computer Science. Ph.D. students work closely with one or more faculty members who serve as research directors and mentors. 

The Ph.D. degree is intended for students planning to pursue a career in computer science research and education. The doctoral program normally requires four to six years of study. Ph.D. students must spend at least four semesters in full-time study at Rice, where full-time study is defined as enrollment in nine or more hours of coursework. The Ph.D. degree requires a combination of coursework and original research, as evidenced in a written thesis and a public oral defense of that thesis.

Our multi-disciplinary research activities often go beyond engineering, as we begin to observe a major X+CS worldwide movement in which innovations in any discipline X occur at the boundary between X and Computer Science.

The Office of Graduate and Postdoctoral Studies and the Rice General Announcements are definitive sources of general information related to obtaining a graduate degree from Rice. Explore the links below to find out more about financial support options while at Rice, life at Rice, living in Houston and graduate student organizations such as CS GSA. 

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Graduate Degree Programs in Computer Science and Engineering

Computer science and engineering doctor of philosophy degree.

All students in the Ph.D. program are required to take Research Experience in Computer Science and Engineering and the course must be completed within the first two regular semesters after entering the Ph.D. program with a grade of B or higher, and to pass a written candidacy examination within the first three regular semesters, offered at the beginning of each fall and spring semester. The examination tests the student’s background preparation and problem-solving ability.

Master of Science in Computer Science and Engineering

The Master of Science in Computer Science and Engineering programs requires the completion of 30 credits. Students interested in an M.S. in CSE should have already successfully completed Operating Systems Design and Construction, Introduction to Computer Architecture, Programming Language Concepts, Data Structures and Algorithms, and Logical Design of Digital Systems or Introduction to the Theory of Computation.

Please click here for a comprehensive list of all CSE graduate courses. Note that not all classes are available every semester.

One-year Master of Engineering in Computer Science and Engineering

The one-year intensive master’s degree program is meant to prepare students for work in industry. As such, there is no thesis required, although a final paper is required during the last semester of the program.

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The School of Electrical Engineering and Computer Science was created in the spring of 2015 to allow greater access to courses offered by both departments for undergraduate and graduate students in exciting collaborative research fields.

We offer B.S. degrees in electrical engineering, computer science, computer engineering and data science and graduate degrees (master's degrees and Ph.D.'s) in electrical engineering and computer science and engineering. EECS focuses on the convergence of technologies and disciplines to meet today’s industrial demands.

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School of Electrical Engineering and Computer Science

The Pennsylvania State University

207 Electrical Engineering West

University Park, PA 16802

814-863-6740

Department of Computer Science and Engineering

814-865-9505

Department of Electrical Engineering

814-865-7667

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UCLA Graduate Programs

Graduate Program: Computer Science

UCLA's Graduate Program in Computer Science offers the following degree(s):

Master of Science (M.S.)

Doctor of Philosophy (Ph.D.)

With questions not answered here or on the program’s site (above), please contact the program directly.

Computer Science Graduate Program at UCLA 404 Westwood Plaza Engineering IV, Room 291 Box 951596 Los Angeles, CA 90095-1596

Visit the Computer Science’s faculty roster

COURSE DESCRIPTIONS

Visit the registrar's site for the Computer Science’s course descriptions

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(310) 825-0060

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MAJOR CODE: COMPUTER SCIENCE