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Home > CICS > CS > CS_DISS

Computer Science Department Dissertations Collection

Dissertations from 2024 2024.

Enabling Privacy and Trust in Edge AI Systems , Akanksha Atrey, Computer Science

Generative Language Models for Personalized Information Understanding , Pengshan Cai, Computer Science

Towards Automatic and Robust Variational Inference , Tomas Geffner, Computer Science

Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping , Samer Nashed, Computer Science

Policy Gradient Methods: Analysis, Misconceptions, and Improvements , Christopher P. Nota, Computer Science

Data to science with AI and human-in-the-loop , Gustavo Perez Sarabia, Computer Science

Question Answering By Case-Based Reasoning With Textual Evidence , Dung N. Thai, Computer Science

Dissertations from 2023 2023

An Introspective Approach for Competence-Aware Autonomy , Connor Basich, Computer Science

Foundations of Node Representation Learning , Sudhanshu Chanpuriya, Computer Science

Learning to See with Minimal Human Supervision , Zezhou Cheng, Computer Science

IMPROVING USER EXPERIENCE BY OPTIMIZING CLOUD SERVICES , Ishita Dasgupta, Computer Science

Automating the Formal Verification of Software , Emily First, Computer Science

Learning from Sequential User Data: Models and Sample-efficient Algorithms , Aritra Ghosh, Computer Science

Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data , Mahmood Jasim, Computer Science

Rigorous Experimentation For Reinforcement Learning , Scott M. Jordan, Computer Science

Towards Robust Long-form Text Generation Systems , Kalpesh Krishna, Computer Science

Emerging Trustworthiness Issues in Distributed Learning Systems , Hamid Mozaffari, Computer Science

TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP , Milad nasresfahani, Computer Science

Evidence Assisted Learning for Clinical Decision Support Systems , Bhanu Pratap Singh Rawat, Computer Science

DESIGN AND ANALYSIS OF CONTENT CACHING SYSTEMS , Anirudh Sabnis, Computer Science

Quantifying and Enhancing the Security of Federated Learning , Virat Vishnu Shejwalkar, Computer Science

Effective and Efficient Transfer Learning in the Era of Large Language Models , Tu Vu, Computer Science

Data-driven Modeling and Analytics for Greening the Energy Ecosystem , John Wamburu, Computer Science

Bayesian Structural Causal Inference with Probabilistic Programming , Sam A. Witty, Computer Science

LEARNING TO RIG CHARACTERS , Zhan Xu, Computer Science

GRAPH REPRESENTATION LEARNING WITH BOX EMBEDDINGS , Dongxu Zhang, Computer Science

Dissertations from 2022 2022

COMBINATORIAL ALGORITHMS FOR GRAPH DISCOVERY AND EXPERIMENTAL DESIGN , Raghavendra K. Addanki, Computer Science

MEASURING NETWORK INTERFERENCE AND MITIGATING IT WITH DNS ENCRYPTION , Seyed Arian Akhavan Niaki, Computer Science

Few-Shot Natural Language Processing by Meta-Learning Without Labeled Data , Trapit Bansal, Computer Science

Communicative Information Visualizations: How to make data more understandable by the general public , Alyxander Burns, Computer Science

REINFORCEMENT LEARNING FOR NON-STATIONARY PROBLEMS , Yash Chandak, Computer Science

Modeling the Multi-mode Distribution in Self-Supervised Language Models , Haw-Shiuan Chang, Computer Science

Nonparametric Contextual Reasoning for Question Answering over Large Knowledge Bases , Rajarshi Das, Computer Science

Languages and Compilers for Writing Efficient High-Performance Computing Applications , Abhinav Jangda, Computer Science

Controllable Neural Synthesis for Natural Images and Vector Art , Difan Liu, Computer Science

Probabilistic Commonsense Knowledge , Xiang Li, Computer Science

DISTRIBUTED LEARNING ALGORITHMS: COMMUNICATION EFFICIENCY AND ERROR RESILIENCE , Raj Kumar Maity, Computer Science

Practical Methods for High-Dimensional Data Publication with Differential Privacy , Ryan H. McKenna, Computer Science

Incremental Non-Greedy Clustering at Scale , Nicholas Monath, Computer Science

High-Quality Automatic Program Repair , Manish Motwani, Computer Science

Unobtrusive Assessment of Upper-Limb Motor Impairment Using Wearable Inertial Sensors , Brandon R. Oubre, Computer Science

Mixture Models in Machine Learning , Soumyabrata Pal, Computer Science

Decision Making with Limited Data , Kieu My Phan, Computer Science

Neural Approaches for Language-Agnostic Search and Recommendation , Hamed Rezanejad Asl Bonab, Computer Science

Low Resource Language Understanding in Voice Assistants , Subendhu Rongali, Computer Science

Enabling Daily Tracking of Individual’s Cognitive State With Eyewear , Soha Rostaminia, Computer Science

LABELED MODULES IN PROGRAMS THAT EVOLVE , Anil K. Saini, Computer Science

Reliable Decision-Making with Imprecise Models , Sandhya Saisubramanian, Computer Science

Data Scarcity in Event Analysis and Abusive Language Detection , Sheikh Muhammad Sarwar, Computer Science

Representation Learning for Shape Decomposition, By Shape Decomposition , Gopal Sharma, Computer Science

Metareasoning for Planning and Execution in Autonomous Systems , Justin Svegliato, Computer Science

Approximate Bayesian Deep Learning for Resource-Constrained Environments , Meet Prakash Vadera, Computer Science

ANSWER SIMILARITY GROUPING AND DIVERSIFICATION IN QUESTION ANSWERING SYSTEMS , Lakshmi Nair Vikraman, Computer Science

Dissertations from 2021 2021

Neural Approaches to Feedback in Information Retrieval , Keping Bi, Computer Science

Sociolinguistically Driven Approaches for Just Natural Language Processing , Su Lin Blodgett, Computer Science

Enabling Declarative and Scalable Prescriptive Analytics in Relational Data , Matteo Brucato, Computer Science

Neural Methods for Answer Passage Retrieval over Sparse Collections , Daniel Cohen, Computer Science

Utilizing Graph Structure for Machine Learning , Stefan Dernbach, Computer Science

Enhancing Usability and Explainability of Data Systems , Anna Fariha, Computer Science

Algorithms to Exploit Data Sparsity , Larkin H. Flodin, Computer Science

3D Shape Understanding and Generation , Matheus Gadelha, Computer Science

Robust Algorithms for Clustering with Applications to Data Integration , Sainyam Galhotra, Computer Science

Improving Evaluation Methods for Causal Modeling , Amanda Gentzel, Computer Science

SAFE AND PRACTICAL MACHINE LEARNING , Stephen J. Giguere, Computer Science

COMPACT REPRESENTATIONS OF UNCERTAINTY IN CLUSTERING , Craig Stuart Greenberg, Computer Science

Natural Language Processing for Lexical Corpus Analysis , Abram Kaufman Handler, Computer Science

Social Measurement and Causal Inference with Text , Katherine A. Keith, Computer Science

Concentration Inequalities in the Wild: Case Studies in Blockchain & Reinforcement Learning , A. Pinar Ozisik, Computer Science

Resource Allocation in Distributed Service Networks , Nitish Kumar Panigrahy, Computer Science

History Modeling for Conversational Information Retrieval , Chen Qu, Computer Science

Design and Implementation of Algorithms for Traffic Classification , Fatemeh Rezaei, Computer Science

SCALING DOWN THE ENERGY COST OF CONNECTING EVERYDAY OBJECTS TO THE INTERNET , Mohammad Rostami, Computer Science

Deep Learning Models for Irregularly Sampled and Incomplete Time Series , Satya Narayan Shukla, Computer Science

Traffic engineering in planet-scale cloud networks , Rachee Singh, Computer Science

Video Adaptation for High-Quality Content Delivery , Kevin Spiteri, Computer Science

Learning from Limited Labeled Data for Visual Recognition , Jong-Chyi Su, Computer Science

Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications , Amee Trivedi, Computer Science

Geometric Representation Learning , Luke Vilnis, Computer Science

Understanding of Visual Domains via the Lens of Natural Language , Chenyun Wu, Computer Science

Towards Practical Differentially Private Mechanism Design and Deployment , Dan Zhang, Computer Science

Audio-driven Character Animation , Yang Zhou, Computer Science

Dissertations from 2020 2020

Noise-Aware Inference for Differential Privacy , Garrett Bernstein, Computer Science

Motion Segmentation - Segmentation of Independently Moving Objects in Video , Pia Katalin Bideau, Computer Science

An Empirical Assessment of the Effectiveness of Deception for Cyber Defense , Kimberly J. Ferguson-Walter, Computer Science

Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems , Richard Freedman, Computer Science

Improving Reinforcement Learning Techniques by Leveraging Prior Experience , Francisco M. Garcia, Computer Science

Optimization and Training of Generational Garbage Collectors , Nicholas Jacek, Computer Science

Understanding the Dynamic Visual World: From Motion to Semantics , Huaizu Jiang, Computer Science

Improving Face Clustering in Videos , SouYoung Jin, Computer Science

Reasoning About User Feedback Under Identity Uncertainty in Knowledge Base Construction , Ariel Kobren, Computer Science

Learning Latent Characteristics of Data and Models using Item Response Theory , John P. Lalor, Computer Science

Higher-Order Representations for Visual Recognition , Tsung-Yu Lin, Computer Science

Learning from Irregularly-Sampled Time Series , Steven Cheng-Xian Li, Computer Science

Dynamic Composition of Functions for Modular Learning , Clemens GB Rosenbaum, Computer Science

Improving Visual Recognition With Unlabeled Data , Aruni Roy Chowdhury, Computer Science

Deep Neural Networks for 3D Processing and High-Dimensional Filtering , Hang Su, Computer Science

Towards Optimized Traffic Provisioning and Adaptive Cache Management for Content Delivery , Aditya Sundarrajan, Computer Science

The Limits of Location Privacy in Mobile Devices , Keen Yuun Sung, Computer Science

ALGORITHMS FOR MASSIVE, EXPENSIVE, OR OTHERWISE INCONVENIENT GRAPHS , David Tench, Computer Science

System Design for Digital Experimentation and Explanation Generation , Emma Tosch, Computer Science

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Digital Commons @ USF > College of Engineering > Computer Science and Engineering > Theses and Dissertations

Computer Science and Engineering Theses and Dissertations

Theses/dissertations from 2023 2023.

Refining the Machine Learning Pipeline for US-based Public Transit Systems , Jennifer Adorno

Insect Classification and Explainability from Image Data via Deep Learning Techniques , Tanvir Hossain Bhuiyan

Brain-Inspired Spatio-Temporal Learning with Application to Robotics , Thiago André Ferreira Medeiros

Evaluating Methods for Improving DNN Robustness Against Adversarial Attacks , Laureano Griffin

Analyzing Multi-Robot Leader-Follower Formations in Obstacle-Laden Environments , Zachary J. Hinnen

Secure Lightweight Cryptographic Hardware Constructions for Deeply Embedded Systems , Jasmin Kaur

A Psychometric Analysis of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr.

Graph Analysis on Social Networks , Shen Lu

Deep Learning-based Automatic Stereology for High- and Low-magnification Images , Hunter Morera

Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai Ng Lugo

Automated Approaches to Enable Innovative Civic Applications from Citizen Generated Imagery , Hye Seon Yi

Theses/Dissertations from 2022 2022

Towards High Performing and Reliable Deep Convolutional Neural Network Models for Typically Limited Medical Imaging Datasets , Kaoutar Ben Ahmed

Task Progress Assessment and Monitoring Using Self-Supervised Learning , Sainath Reddy Bobbala

Towards More Task-Generalized and Explainable AI Through Psychometrics , Alec Braynen

A Multiple Input Multiple Output Framework for the Automatic Optical Fractionator-based Cell Counting in Z-Stacks Using Deep Learning , Palak Dave

On the Reliability of Wearable Sensors for Assessing Movement Disorder-Related Gait Quality and Imbalance: A Case Study of Multiple Sclerosis , Steven Díaz Hernández

Securing Critical Cyber Infrastructures and Functionalities via Machine Learning Empowered Strategies , Tao Hou

Social Media Time Series Forecasting and User-Level Activity Prediction with Gradient Boosting, Deep Learning, and Data Augmentation , Fred Mubang

A Study of Deep Learning Silhouette Extractors for Gait Recognition , Sneha Oladhri

Analyzing Decision-making in Robot Soccer for Attacking Behaviors , Justin Rodney

Generative Spatio-Temporal and Multimodal Analysis of Neonatal Pain , Md Sirajus Salekin

Secure Hardware Constructions for Fault Detection of Lattice-based Post-quantum Cryptosystems , Ausmita Sarker

Adaptive Multi-scale Place Cell Representations and Replay for Spatial Navigation and Learning in Autonomous Robots , Pablo Scleidorovich

Predicting the Number of Objects in a Robotic Grasp , Utkarsh Tamrakar

Humanoid Robot Motion Control for Ramps and Stairs , Tommy Truong

Preventing Variadic Function Attacks Through Argument Width Counting , Brennan Ward

Theses/Dissertations from 2021 2021

Knowledge Extraction and Inference Based on Visual Understanding of Cooking Contents , Ahmad Babaeian Babaeian Jelodar

Efficient Post-Quantum and Compact Cryptographic Constructions for the Internet of Things , Rouzbeh Behnia

Efficient Hardware Constructions for Error Detection of Post-Quantum Cryptographic Schemes , Alvaro Cintas Canto

Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation During Dimensionality Reduction , Curtis Thomas Davis

Design, Deployment, and Validation of Computer Vision Techniques for Societal Scale Applications , Arup Kanti Dey

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing , Hamza Elhamdadi

Automatic Detection of Vehicles in Satellite Images for Economic Monitoring , Cole Hill

Analysis of Contextual Emotions Using Multimodal Data , Saurabh Hinduja

Data-driven Studies on Social Networks: Privacy and Simulation , Yasanka Sameera Horawalavithana

Automated Identification of Stages in Gonotrophic Cycle of Mosquitoes Using Computer Vision Techniques , Sherzod Kariev

Exploring the Use of Neural Transformers for Psycholinguistics , Antonio Laverghetta Jr.

Secure VLSI Hardware Design Against Intellectual Property (IP) Theft and Cryptographic Vulnerabilities , Matthew Dean Lewandowski

Turkic Interlingua: A Case Study of Machine Translation in Low-resource Languages , Jamshidbek Mirzakhalov

Automated Wound Segmentation and Dimension Measurement Using RGB-D Image , Chih-Yun Pai

Constructing Frameworks for Task-Optimized Visualizations , Ghulam Jilani Abdul Rahim Quadri

Trilateration-Based Localization in Known Environments with Object Detection , Valeria M. Salas Pacheco

Recognizing Patterns from Vital Signs Using Spectrograms , Sidharth Srivatsav Sribhashyam

Recognizing Emotion in the Wild Using Multimodal Data , Shivam Srivastava

A Modular Framework for Multi-Rotor Unmanned Aerial Vehicles for Military Operations , Dante Tezza

Human-centered Cybersecurity Research — Anthropological Findings from Two Longitudinal Studies , Anwesh Tuladhar

Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy , Troi André Williams

Human-centric Cybersecurity Research: From Trapping the Bad Guys to Helping the Good Ones , Armin Ziaie Tabari

Theses/Dissertations from 2020 2020

Classifying Emotions with EEG and Peripheral Physiological Data Using 1D Convolutional Long Short-Term Memory Neural Network , Rupal Agarwal

Keyless Anti-Jamming Communication via Randomized DSSS , Ahmad Alagil

Active Deep Learning Method to Automate Unbiased Stereology Cell Counting , Saeed Alahmari

Composition of Atomic-Obligation Security Policies , Yan Cao Albright

Action Recognition Using the Motion Taxonomy , Maxat Alibayev

Sentiment Analysis in Peer Review , Zachariah J. Beasley

Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication , Dmitrii Cherezov

Feature Selection Via Random Subsets Of Uncorrelated Features , Long Kim Dang

Unifying Security Policy Enforcement: Theory and Practice , Shamaria Engram

PsiDB: A Framework for Batched Query Processing and Optimization , Mehrad Eslami

Composition of Atomic-Obligation Security Policies , Danielle Ferguson

Algorithms To Profile Driver Behavior From Zero-permission Embedded Sensors , Bharti Goel

The Efficiency and Accuracy of YOLO for Neonate Face Detection in the Clinical Setting , Jacqueline Hausmann

Beyond the Hype: Challenges of Neural Networks as Applied to Social Networks , Anthony Hernandez

Privacy-Preserving and Functional Information Systems , Thang Hoang

Managing Off-Grid Power Use for Solar Fueled Residences with Smart Appliances, Prices-to-Devices and IoT , Donnelle L. January

Novel Bit-Sliced In-Memory Computing Based VLSI Architecture for Fast Sobel Edge Detection in IoT Edge Devices , Rajeev Joshi

Edge Computing for Deep Learning-Based Distributed Real-time Object Detection on IoT Constrained Platforms at Low Frame Rate , Lakshmikavya Kalyanam

Establishing Topological Data Analysis: A Comparison of Visualization Techniques , Tanmay J. Kotha

Machine Learning for the Internet of Things: Applications, Implementation, and Security , Vishalini Laguduva Ramnath

System Support of Concurrent Database Query Processing on a GPU , Hao Li

Deep Learning Predictive Modeling with Data Challenges (Small, Big, or Imbalanced) , Renhao Liu

Countermeasures Against Various Network Attacks Using Machine Learning Methods , Yi Li

Towards Safe Power Oversubscription and Energy Efficiency of Data Centers , Sulav Malla

Design of Support Measures for Counting Frequent Patterns in Graphs , Jinghan Meng

Automating the Classification of Mosquito Specimens Using Image Processing Techniques , Mona Minakshi

Models of Secure Software Enforcement and Development , Hernan M. Palombo

Functional Object-Oriented Network: A Knowledge Representation for Service Robotics , David Andrés Paulius Ramos

Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning , Rahul Paul

Algorithms and Framework for Computing 2-body Statistics on Graphics Processing Units , Napath Pitaksirianan

Efficient Viewshed Computation Algorithms On GPUs and CPUs , Faisal F. Qarah

Relational Joins on GPUs for In-Memory Database Query Processing , Ran Rui

Micro-architectural Countermeasures for Control Flow and Misspeculation Based Software Attacks , Love Kumar Sah

Efficient Forward-Secure and Compact Signatures for the Internet of Things (IoT) , Efe Ulas Akay Seyitoglu

Detecting Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure via Cough and Wheezing Sounds Using Smart-Phones and Machine Learning , Anthony Windmon

Toward Culturally Relevant Emotion Detection Using Physiological Signals , Khadija Zanna

Theses/Dissertations from 2019 2019

Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation , Sathyanarayanan Narasimhan Aakur

Empirical Analysis of a Cybersecurity Scoring System , Jaleel Ahmed

Phenomena of Social Dynamics in Online Games , Essa Alhazmi

A Machine Learning Approach to Predicting Community Engagement on Social Media During Disasters , Adel Alshehri

Interactive Fitness Domains in Competitive Coevolutionary Algorithm , ATM Golam Bari

Measuring Influence Across Social Media Platforms: Empirical Analysis Using Symbolic Transfer Entropy , Abhishek Bhattacharjee

A Communication-Centric Framework for Post-Silicon System-on-chip Integration Debug , Yuting Cao

Authentication and SQL-Injection Prevention Techniques in Web Applications , Cagri Cetin

Multimodal Emotion Recognition Using 3D Facial Landmarks, Action Units, and Physiological Data , Diego Fabiano

Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations , Yongqiang Huang

A GPU-Based Framework for Parallel Spatial Indexing and Query Processing , Zhila Nouri Lewis

A Flexible, Natural Deduction, Automated Reasoner for Quick Deployment of Non-Classical Logic , Trisha Mukhopadhyay

An Efficient Run-time CFI Check for Embedded Processors to Detect and Prevent Control Flow Based Attacks , Srivarsha Polnati

Force Feedback and Intelligent Workspace Selection for Legged Locomotion Over Uneven Terrain , John Rippetoe

Detecting Digitally Forged Faces in Online Videos , Neilesh Sambhu

Malicious Manipulation in Service-Oriented Network, Software, and Mobile Systems: Threats and Defenses , Dakun Shen

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Home > FACULTIES > Computer Science > CSD-ETD

Computer Science Theses and Dissertations

This collection contains theses and dissertations from the Department of Computer Science, collected from the Scholarship@Western Electronic Thesis and Dissertation Repository

Theses/Dissertations from 2024 2024

A Target-Based and A Targetless Extrinsic Calibration Methods for Thermal Camera and 3D LiDAR , Farhad Dalirani

Investigating Tree- and Graph-based Neural Networks for Natural Language Processing Applications , Sudipta Singha Roy

Theses/Dissertations from 2023 2023

Classification of DDoS Attack with Machine Learning Architectures and Exploratory Analysis , Amreen Anbar

Multi-view Contrastive Learning for Unsupervised Domain Adaptation in Brain-Computer Interfaces , Sepehr Asgarian

Improved Protein Sequence Alignments Using Deep Learning , Seyed Sepehr Ashrafzadeh

INVESTIGATING IMPROVEMENTS TO MESH INDEXING , Anurag Bhattacharjee

Algorithms and Software for Oligonucleotide Design , Qin Dong

Framework for Assessing Information System Security Posture Risks , Syed Waqas Hamdani

De novo sequencing of multiple tandem mass spectra of peptide containing SILAC labeling , Fang Han

Local Model Agnostic XAI Methodologies Applied to Breast Cancer Malignancy Predictions , Heather Hartley

A Quantitative Analysis Between Software Quality Posture and Bug-fixing Commit , Rongji He

A Novel Method for Assessment of Batch Effect on single cell RNA sequencing data , Behnam Jabbarizadeh

Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs , Taabish Jeshani

Citation Polarity Identification From Scientific Articles Using Deep Learning Methods , Souvik Kundu

Denoising-Based Domain Adaptation Network for EEG Source Imaging , Runze Li

Decoy-Target Database Strategy and False Discovery Rate Analysis for Glycan Identification , Xiaoou Li

DpNovo: A DEEP LEARNING MODEL COMBINED WITH DYNAMIC PROGRAMMING FOR DE NOVO PEPTIDE SEQUENCING , Yizhou Li

Developing A Smart Home Surveillance System Using Autonomous Drones , Chongju Mai

Look-Ahead Selective Plasticity for Continual Learning , Rouzbeh Meshkinnejad

The Two Visual Processing Streams Through The Lens Of Deep Neural Networks , Aidasadat Mirebrahimi Tafreshi

Source-free Domain Adaptation for Sleep Stage Classification , Yasmin Niknam

Data Heterogeneity and Its Implications for Fairness , Ghazaleh Noroozi

Enhancing Urban Life: A Policy-Based Autonomic Smart City Management System for Efficient, Sustainable, and Self-Adaptive Urban Environments , Elham Okhovat

Evaluating the Likelihood of Bug Inducing Commits Using Metrics Trend Analysis , Parul Parul

On Computing Optimal Repairs for Conditional Independence , Alireza Pirhadi

Open-Set Source-Free Domain Adaptation in Fundus Images Analysis , Masoud Pourreza

Migration in Edge Computing , Arshin Rezazadeh

A Modified Hopfield Network for the K-Median Problem , Cody Rossiter

Predicting Network Failures with AI Techniques , Chandrika Saha

Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective , Sajal Saha

An Exploration of Visual Analytic Techniques for XAI: Applications in Clinical Decision Support , Mozhgan Salimiparsa

Attention-based Multi-Source-Free Domain Adaptation for EEG Emotion Recognition , Amir Hesam Salimnia

Global Cyber Attack Forecast using AI Techniques , Nusrat Kabir Samia

IMPLEMENTATION OF A PRE-ASSESSMENT MODULE TO IMPROVE THE INITIAL PLAYER EXPERIENCE USING PREVIOUS GAMING INFORMATION , Rafael David Segistan Canizales

A Computational Framework For Identifying Relevant Cell Types And Specific Regulatory Mechanisms In Schizophrenia Using Data Integration Methods , Kayvan Shabani

Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network , Sareh Soltani Nejad

Smartphone Loss Prevention System Using BLE and GPS Technology , Noshin Tasnim

A Hybrid Continual Machine Learning Model for Efficient Hierarchical Classification of Domain-Specific Text in The Presence of Class Overlap (Case Study: IT Support Tickets) , Yasmen M. Wahba

Reducing Negative Transfer of Random Data in Source-Free Unsupervised Domain Adaptation , Anthony Wong

Deep Neural Methods for True/Pseudo- Invasion Classification in Colorectal Polyp Whole-Slide Images , Zhiyuan Yang

Developing a Relay-based Autonomous Drone Delivery System , Muhammad Zakar

Learning Mortality Risk for COVID-19 Using Machine Learning and Statistical Methods , Shaoshi Zhang

Machine Learning Techniques for Improved Functional Brain Parcellation , Da Zhi

Theses/Dissertations from 2022 2022

The Design and Implementation of a High-Performance Polynomial System Solver , Alexander Brandt

Defining Service Level Agreements in Serverless Computing , Mohamed Elsakhawy

Algorithms for Regular Chains of Dimension One , Juan P. Gonzalez Trochez

Towards a Novel and Intelligent e-commerce Framework for Smart-Shopping Applications , Susmitha Hanumanthu

Multi-Device Data Analysis for Fault Localization in Electrical Distribution Grids , Jacob D L Hunte

Towards Parking Lot Occupancy Assessment Using Aerial Imagery and Computer Vision , John Jewell

Potential of Vision Transformers for Advanced Driver-Assistance Systems: An Evaluative Approach , Andrew Katoch

Psychological Understanding of Textual journals using Natural Language Processing approaches , Amirmohammad Kazemeinizadeh

Driver Behavior Analysis Based on Real On-Road Driving Data in the Design of Advanced Driving Assistance Systems , Nima Khairdoost

Solving Challenges in Deep Unsupervised Methods for Anomaly Detection , Vahid Reza Khazaie

Developing an Efficient Real-Time Terrestrial Infrastructure Inspection System Using Autonomous Drones and Deep Learning , Marlin Manka

Predictive Modelling For Topic Handling Of Natural Language Dialogue With Virtual Agents , Lareina Milambiling

Improving Deep Entity Resolution by Constraints , Soudeh Nilforoushan

Respiratory Pattern Analysis for COVID-19 Digital Screening Using AI Techniques , Annita Tahsin Priyoti

Extracting Microservice Dependencies Using Log Analysis , Andres O. Rodriguez Ishida

False Discovery Rate Analysis for Glycopeptide Identification , Shun Saito

Towards a Generalization of Fulton's Intersection Multiplicity Algorithm , Ryan Sandford

An Investigation Into Time Gazed At Traffic Objects By Drivers , Kolby R. Sarson

Exploring Artificial Intelligence (AI) Techniques for Forecasting Network Traffic: Network QoS and Security Perspectives , Ibrahim Mohammed Sayem

A Unified Representation and Deep Learning Architecture for Persuasive Essays in English , Muhammad Tawsif Sazid

Towards the development of a cost-effective Image-Sensing-Smart-Parking Systems (ISenSmaP) , Aakriti Sharma

Advances in the Automatic Detection of Optimization Opportunities in Computer Programs , Delaram Talaashrafi

Reputation-Based Trust Assessment of Transacting Service Components , Konstantinos Tsiounis

Fully Autonomous UAV Exploration in Confined and Connectionless Environments , Kirk P. Vander Ploeg

Three Contributions to the Theory and Practice of Optimizing Compilers , Linxiao Wang

Developing Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach , Wumian Wang

Predicting and Modifying Memorability of Images , Mohammad Younesi

Theses/Dissertations from 2021 2021

Generating Effective Sentence Representations: Deep Learning and Reinforcement Learning Approaches , Mahtab Ahmed

A Physical Layer Framework for a Smart City Using Accumulative Bayesian Machine Learning , Razan E. AlFar

Load Balancing and Resource Allocation in Smart Cities using Reinforcement Learning , Aseel AlOrbani

Contrastive Learning of Auditory Representations , Haider Al-Tahan

Cache-Friendly, Modular and Parallel Schemes For Computing Subresultant Chains , Mohammadali Asadi

Protein Interaction Sites Prediction using Deep Learning , Sourajit Basak

Predicting Stock Market Sector Sentiment Through News Article Based Textual Analysis , William A. Beldman

Improving Reader Motivation with Machine Learning , Tanner A. Bohn

A Black-box Approach for Containerized Microservice Monitoring in Fog Computing , Shi Chang

Visualization and Interpretation of Protein Interactions , Dipanjan Chatterjee

A Framework for Characterising Performance in Multi-Class Classification Problems with Applications in Cancer Single Cell RNA Sequencing , Erik R. Christensen

Exploratory Search with Archetype-based Language Models , Brent D. Davis

Evolutionary Design of Search and Triage Interfaces for Large Document Sets , Jonathan A. Demelo

Building Effective Network Security Frameworks using Deep Transfer Learning Techniques , Harsh Dhillon

A Deep Topical N-gram Model and Topic Discovery on COVID-19 News and Research Manuscripts , Yuan Du

Automatic extraction of requirements-related information from regulatory documents cited in the project contract , Sara Fotouhi

Developing a Resource and Energy Efficient Real-time Delivery Scheduling Framework for a Network of Autonomous Drones , Gopi Gugan

A Visual Analytics System for Rapid Sensemaking of Scientific Documents , Amirreza Haghverdiloo Barzegar

Calibration Between Eye Tracker and Stereoscopic Vision System Employing a Linear Closed-Form Perspective-n-Point (PNP) Algorithm , Mohammad Karami

Fuzzy and Probabilistic Rule-Based Approaches to Identify Fault Prone Files , Piyush Kumar Korlepara

Parallel Arbitrary-precision Integer Arithmetic , Davood Mohajerani

A Technique for Evaluating the Health Status of a Software Module Using Process Metrics , . Ria

Visual Analytics for Performing Complex Tasks with Electronic Health Records , Neda Rostamzadeh

Predictive Model of Driver's Eye Fixation for Maneuver Prediction in the Design of Advanced Driving Assistance Systems , Mohsen Shirpour

A Generative-Discriminative Approach to Human Brain Mapping , Deepanshu Wadhwa

WesternAccelerator：Rapid Development of Microservices , Haoran Wei

A Lightweight and Explainable Citation Recommendation System , Juncheng Yin

Mitosis Detection from Pathology Images , Jinhang Zhang

Theses/Dissertations from 2020 2020

Visual Analytics of Electronic Health Records with a focus on Acute Kidney Injury , Sheikh S. Abdullah

Towards the Development of Network Service Cost Modeling-An ISP Perspective , Yasmeen Ali

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Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023

Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023

Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023

Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

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Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

Research areas

Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

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Ai generates high-quality images 30 times faster in a single step.

Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.

Optimizing nuclear fuels for next-generation reactors

While working to nurture scientific talent in his native Nigeria, Assistant Professor Ericmoore Jossou is setting his sights on using materials science and computation to design robust nuclear components.

2024 MacVicar Faculty Fellows named

Professors Berggren, Campbell, Pollock, and Vaikuntanathan are honored for exceptional undergraduate teaching.

Priya Donti named AI2050 Early Career Fellow

Assistant Professor Priya Donti has been named an AI2050 Early Career Fellow by Schmidt Sciences, a philanthropic initiative from Eric and Wendy Schmidt aimed at helping to solve hard problems in AI. 

Using generative AI to improve software testing

MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.

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Academic Catalog 2023-2024

Computer science, phd.

The PhD in Computer Science is designed to prepare students for careers in academia, industrial and national research labs, and technical leadership in industry and government. The rigorous curriculum provides a broad background in the fundamentals of computer science, advanced courses in a wide range of focus areas, and opportunity to make an impact at the forefront of computing. The program provides training in conducting research, publishing and presenting papers, developing systems, and establishing science and technology policy.

A minimum of 48 semester hours of coursework beyond the BS/BA degree is required of all students.

All students must demonstrate sufficient knowledge in the fundamentals of computer science, as well as the ability to carry out research in an area of computer science.

The student must maintain a minimum grade-point average of 3.500 among the six core courses and receive a grade of B or better in each of these courses. Students who have taken equivalent courses in other institutions may petition to be exempted from the course(s) (subject to the approval of the PhD computer science curriculum committee). Each student may repeat a course once for no more than three out of the six courses if they do not receive a B or better in the course. Students with a Master of Science in Computer Science may petition to the PhD computer science curriculum committee for an exemption from these courses. Petition forms are available on the college website.

The fields listed do not necessarily represent areas of specialization or separate tracks within the PhD program. Rather, they attempt to delineate areas on which the student must be examined in order to measure their ability to complete the degree. Therefore, they may be adjusted in the future to reflect changes in the discipline of computer science and in faculty interests within the Khoury College of Computer Sciences. Similarly, these fields do not represent the only areas in which a student may write their dissertation. They are, however, intended to serve as a basis for performing fundamental research in computer science.

Paper Requirement

To demonstrate research ability, the student is required to submit to the PhD committee a research or a survey paper in an area of specialty under the supervision of a faculty advisor.  A submitted paper from a student is considered to have fulfilled the paper requirement if:

• The paper has been accepted by a selective conference.
• The student has made a substantial contribution to the paper.
• The advisor has endorsed the paper with a written statement indicating the student’s contribution.
• The PhD computer science curriculum committee has voted on a positive recommendation. The committee may require a presentation from the student before making a recommendation.

Admission to Candidacy

Upon completion of the course and the research paper requirements, the student is admitted to candidacy for the PhD degree. It is highly recommended that the student complete the candidacy requirement by the end of their second year but no later than the third year.

One year of continuous full-time study is required after admission to the PhD candidacy. It is expected that during this period the student will make substantial progress in preparing for the comprehensive examination.

Teaching Requirement

All computer science PhD students must satisfy the teaching requirement in order to graduate. This requirement is fulfilled when the student works as a teaching assistant or instructor of record for one semester and during this semester:

• Teaches at least three hours of classes
• Prepares at least one assignment, or quiz, or equivalent

PhD students are expected to satisfy the teaching requirement some time after completing their first year and at least one semester prior to scheduling their PhD defense. 

Comprehensive Examination/Dissertation Proposal

After the student has achieved sufficient depth in a field of study, they prepare a proposal for the PhD dissertation. This process should take place no later than the end of the fifth year in residence. The student prepares a dissertation proposal, which describes the proposed research, including the relevant background materials from the literature. The proposal should clearly specify the research problems to be attacked, the techniques to be used, and a schedule of milestones toward completion.

The dissertation proposal must be approved by the dissertation committee. With the help of the advisor, a student selects the committee, consisting of at least four members, to be approved by the PhD computer science curriculum committee. The four members must include the advisor, two internal members, and an external member.

Upon approval of the written proposal, the student has to present the proposed work orally in a public forum, followed by a closed-door oral examination from the dissertation committee. The student may take the dissertation proposal examination twice, at most.

Doctoral Dissertation

Upon successful completion of solving the research proposed in the dissertation proposal, the candidate has an opportunity to prepare the dissertation for approval by the dissertation committee. The dissertation must contain results of extensive research and make an original contribution to the field of computer science. The work should give evidence of the candidate’s ability to carry out independent research. It is expected that the dissertation should be of sufficient quality to merit publication in a reputable journal in computer science.

Doctoral Committee

With the help of the advisor, a student selects the committee, consisting of at least four members, to be approved by the PhD computer science curriculum committee. The four members must include the advisor, two internal members, and an external member.

Dissertation Defense

The dissertation defense is held in accordance with the regulations of the University Graduate Curriculum Committee. It consists of a lecture given by the candidate on the subject matter of the dissertation. This is followed by questions from the dissertation committee and others in attendance concerning the results of the dissertation as well as any related matters. The defense is chaired by the PhD advisor.

Time and Time Limitation

After the establishment of degree candidacy, a maximum of five years will be allowed for the completion of the degree requirements, unless an extension is granted by the college graduate committee.

Complete all courses and requirements listed below unless otherwise indicated.

Coursework Paper requirement Admission to candidacy Residency Teaching requirement Comprehensive examination/dissertation proposal Doctoral dissertation Doctoral committee Dissertation defense

Course Area Requirements

A grade of B or higher is required in each course. A cumulative 3.500 GPA is required for the core requirement.

Students should refer to the course numbering table for graduate course leveling .

Dissertation

Program credit/gpa requirements.

48 total semester hours required Minimum overall 3.000 GPA required

Sample Curriculum 

Incoming PhD in Computer Science students who have already completed a Master of Science in Computer Science or an adjacent field may petition to the PhD in Computer Science program administration for advanced entry. Advanced entry petitions are reviewed by the program administration on a case-by-case basis. Please note that advanced standing does not waive by itself any part of the PhD coursework requirements.

As a degree conferral requirement, a minimum of 16 semester hours of coursework beyond the 32 semester hours of the master’s degree is required of advanced entry PhD students (48 semester hours is required of standard entry PhD students). Students must maintain a minimum GPA of 3.500 as well as earn a grade of B or better in each course.

Refer to the Computer Science, PhD, overview , for research/survey paper requirements.

Refer to the Computer Science, PhD, overview, for admission to candidacy requirements.

Refer to the Computer Science, PhD, overview, for residency requirements.

Refer to the Computer Science, PhD, overview, for the teaching requirement.

Refer to the Computer Science, PhD, overview, for comprehensive examination requirements.

Annual review Course requirements     Paper requirement         Comprehensive exam Teaching requirement Doctoral candidacy Dissertation committee Dissertation proposal Dissertation defense

Core Requirements

Students must maintain a minimum GPA of 3.500 as well as earn a grade of B or better in each course.

16 total semester hours required Minimum overall 3.500 GPA required

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Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.

A Systematic Literature Review of Empiricism and Norms of Reporting in Computing Education Research Literature

Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories. Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work? Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve. Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature. Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.

Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

A genre-based analysis of questions and comments in Q&A sessions after conference paper presentations in computer science

Gender diversity in computer science at a large public r1 research university: reporting on a self-study.

With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.

Designing for Student-Directedness: How K–12 Teachers Utilize Peers to Support Projects

Student-directed projects—projects in which students have individual control over what they create and how to create it—are a promising practice for supporting the development of conceptual understanding and personal interest in K–12 computer science classrooms. In this article, we explore a central (and perhaps counterintuitive) design principle identified by a group of K–12 computer science teachers who support student-directed projects in their classrooms: in order for students to develop their own ideas and determine how to pursue them, students must have opportunities to engage with other students’ work. In this qualitative study, we investigated the instructional practices of 25 K–12 teachers using a series of in-depth, semi-structured interviews to develop understandings of how they used peer work to support student-directed projects in their classrooms. Teachers described supporting their students in navigating three stages of project development: generating ideas, pursuing ideas, and presenting ideas. For each of these three stages, teachers considered multiple factors to encourage engagement with peer work in their classrooms, including the quality and completeness of shared work and the modes of interaction with the work. We discuss how this pedagogical approach offers students new relationships to their own learning, to their peers, and to their teachers and communicates important messages to students about their own competence and agency, potentially contributing to aims within computer science for broadening participation.

Creativity in CS1: A Literature Review

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

CATS: Customizable Abstractive Topic-based Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.

Exploring students’ and lecturers’ views on collaboration and cooperation in computer science courses - a qualitative analysis

Factors affecting student educational choices regarding oer material in computer science, export citation format, share document.

Tips to Become a Better (Computer Science) Ph.D. Student

Why does the world need another blog post.

There are already a lot of great blogs posts about the computer science Ph.D. experience, each approaching it from a different angle (the whole process of a Ph.D., how to choose your research topic, etc.). However, the ideas presented in most of these blog post come from the experience of one person while this blog is a condensed summary of in-depth talks with more than five professors and three Ph.D. student during the YArch workshop at HPCA’19. During these conversations, we discussed topics that are important for early year computer science Ph.D. students . We chose ten ideas we found most impactful to us, and explain five of them in detail and present the other five as short tips.

Research > Courses

Be professional, read a lot and read broadly, impact humankind, don’t give up on your research topic easily, aim for top-tier conferences.

• Use existing resources in your groups

You are powerful!

Focus on publishing.

If you have more ideas, please comment at the bottom of this post!

Other amazing blogs out there:

• The Ph.D. Grind
• Tips: How to Do Research
• So long, and thanks for the Ph.D.!
• Graduate School Survival Guide
• Tips for a New Computer Architecture PhD Student

Young Ph.D. students tend to spend too much time on courses. However, research outweighs courses.

Take courses with a grain of salt

Courses are not as important as they seem to be. The priority of a Ph.D. student is to do research – the earlier you start your research, the better off you’ll be in the long run.

However, don’t go to extremes ! A poor grade can also be a huge problem. You should always be familiar with the requirement of qualification exams or generals and meet all the standards about the courses.

Remember the main ideas of courses

Trapping ourselves in trivial details of a course is easy. However, most of the specifics are not important to our research even if the topic is related to our area.

A good approach is to use what you’ve learned from one course and apply it to a different field (e.g., taking an analysis tool from a compiler course and applying it in computer networks).

Treat your Ph.D. as a job. You get paid (albeit not much) for being a Ph.D. candidate, so make your work worth the money. This professional mindset should also be apparent to your advisor. Some advisors take on a more hands-off approach, for instance letting you work from home, but this is no reason for slacking; you should be responsible for your research schedule, such as reminding your advisor of plans from previous group meetings. Your status is not that of a student but rather that of a peer in the research community.

Though it can be very daunting starting out, reading papers is an essential part of the Ph.D. life. Previously, you may have read papers when it was necessary for a class or a project. However, you should put reading papers in your daily routine. Doing so allows you to draw inspiration from a sea of knowledge and prevents yourself from reinventing the wheel. Besides, it’s a great way to be productive on a slow day.

Make a plan to read

When scheduling your day, assign one period just for reading papers. You can read one paper in depth or compare several papers; regardless of your choice, allotting time to this task is the key.

Read broadly

Reading papers from different subfields of computer science is a great way to learn the jargon, the method, and the mindset of researchers in each field. This can be the first step towards discovering opportunities for collaboration.

It is not uncommon for a Ph.D. student to spend several years building a system that turns out to be fundamentally flawed or not as applicable as expected. Don’t worry! There is nothing wrong with failing, and perhaps we should even expect failure to be part of the journey. But we should aim to fail early in order to have time to work on another project (and graduate!).

Perform a limit study

Perform a quick limit study before sticking with a project. A limit study includes in-depth analyses of implicit assumptions we make when coming up with an idea, a related works search, and the potential of the work if everything goes well. A great limit study can itself be a publishable paper. An example can be found here .

Hacky implementation can be useful

Being a researcher, your work is to develop proof-of-concepts. Nevertheless, you need to demonstrate that your concept is sound for the simplest of cases before continuing to the full-blown system. Hack in the minimum set to show that your idea is possible while resisting the temptation to build a robust infrastructure – if your idea fails, you will know to stop earlier.

Impacting humankind may sound too ambitious, but it should be the ultimate reason why we embark on this journey.

Choose an impactful research topic

In terms of how our Ph.D. research could impact human knowledge, I would like to refer to The Illustrated Guide to a Ph.D. by Matt Might. All we will do in five years is pushing the boundary of human knowledge by a minute margin. Choose a topic that you are able to contribute to, feel passionate about, and can explain the importance of to a layman in a 3-min talk.

Check out why Matt Might changed his research focus from programming languages to precise medicine.

How can our research actually impact people from other fields?

A survey paper by the Liberty Research Group sheds light on how the improvement of programming tools impacts ( computational scientists ) all scientists. Thinking about how your research affects people from other fields can help you define the scope of your contribution.

At some point, we will get bored with our research topic and find something else interesting. Think twice before switching topics. You must differentiate between your project heading nowhere and you getting tired of being stuck.

You should focus on publishing at only top-tier conferences. Don’t consider second-tier venues unless the work has been rejected several times by top-tier conferences. This can prevent you from doing incremental work to make your publication list look better.

Use existing resources in your group

For many fields in computer science, a mature infrastructure requires several years of development by multiple graduate students. Think about how to make use of the infrastructure and resources in the group to boost your research progress.

Even though we are just junior graduate students, we can have a massive impact on ourselves, our group, and even our department. For example, if there is no reading group for your field in your department, start one!

Needless to say, publications are essential since those are what people look at once we graduate.

Acknowledgment

All the ideas in this blog originate from the talks with mentors of the YArch’19 workshop. Thanks to Prof. Boris Grot from the University of Edinburgh, Prof. Thomas Wenisch from the University of Michigan, Prof. Vijay Janapa Reddi from Harvard University, Prof. Luis Ceze from the University of Washington, and Prof. Kevin Skadron from the University of Virginia.

Thanks to two chairs of the YArch’19 workshop, Shaizeen Aga from AMD Research and Prof. Aasheesh Kolli from Pennsylvania State University, for making this possible.

Greg Chan and Bhargav Godala from the Liberty Research Group were at most of these talks and helped me write down some ideas.

6th year Ph.D. student @ Liberty Research Group, Princeton University

Graduated Master @ Liberty Research Group, Princeton University

ScholarWorks

Home > Engineering > Computer Science > Computer Science Graduate Projects

Computer Science Graduate Projects and Theses

Theses/dissertations from 2023 2023.

High-Performance Domain-Specific Library for Hydrologic Data Processing , Kalyan Bhetwal

Verifying Data Provenance During Workflow Execution for Scientific Reproducibility , Rizbanul Hasan

Remote Sensing to Advance Understanding of Snow-Vegetation Relationships and Quantify Snow Depth and Snow Water Equivalent , Ahmad Hojatimalekshah

Exploring the Capability of a Self-Supervised Conditional Image Generator for Image-to-Image Translation without Labeled Data: A Case Study in Mobile User Interface Design , Hailee Kiesecker

Fake News Detection Using Narrative Content and Discourse , Hongmin Kim

Anomaly Detection Using Graph Neural Network , Bishal Lakha

Sparse Format Conversion and Code Synthesis , Tobi Goodness Popoola

Portable Sparse Polyhedral Framework Code Generation Using Multi Level Intermediate Representation , Aaron St. George

Severity Measures for Assessing Error in Automatic Speech Recognition , Ryan Whetten

Theses/Dissertations from 2022 2022

Improved Computational Prediction of Function and Structural Representation of Self-Cleaving Ribozymes with Enhanced Parameter Selection and Library Design , James D. Beck

Meshfree Methods for PDEs on Surfaces , Andrew Michael Jones

Deep Learning of Microstructures , Amir Abbas Kazemzadeh Farizhandi

Long-Term Trends in Extreme Environmental Events with Changepoint Detection , Mintaek Lee

Structure Aware Smart Encoding and Decoding of Information in DNA , Shoshanna Llewellyn

Towards Making Transformer-Based Language Models Learn How Children Learn , Yousra Mahdy

Ontology-Based Formal Approach for Safety and Security Verification of Industrial Control Systems , Ramesh Neupane

Improving Children's Authentication Practices with Respect to Graphical Authentication Mechanism , Dhanush Kumar Ratakonda

Hate Speech Detection Using Textual and User Features , Rohan Raut

Automated Detection of Sockpuppet Accounts in Wikipedia , Mostofa Najmus Sakib

Characterization and Mitigation of False Information on the Web , Anu Shrestha

Sinusoidal Projection for 360° Image Compression and Triangular Discrete Cosine Transform Impact in the JPEG Pipeline , Iker Vazquez Lopez

Theses/Dissertations from 2021 2021

Training Wheels for Web Search: Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom , Garrett Allen

Fair and Efficient Consensus Protocols for Secure Blockchain Applications , Golam Dastoger Bashar

Why Don't You Act Your Age?: Recognizing the Stereotypical 8-12 Year Old Searcher by Their Search Behavior , Michael Green

Ensuring Consistency and Efficiency of the Incremental Unit Network in a Distributed Architecture , Mir Tahsin Imtiaz

Modeling Real and Fake News Sharing in Social Networks , Abishai Joy

Modeling and Analyzing Users' Privacy Disclosure Behavior to Generate Personalized Privacy Policies , A.K.M. Nuhil Mehdy

Into the Unknown: Exploration of Search Engines' Responses to Users with Depression and Anxiety , Ashlee Milton

Generating Test Inputs from String Constraints with an Automata-Based Solver , Marlin Roberts

A Case Study in Representing Scientific Applications ( GeoAc ) Using the Sparse Polyhedral Framework , Ravi Shankar

Actors for the Internet of Things , Arjun Shukla

Theses/Dissertations from 2020 2020

Towards Unifying Grounded and Distributional Semantics Using the Words-as-Classifiers Model of Lexical Semantics , Stacy Black

Improving Scientist Productivity, Architecture Portability, and Performance in ParFlow , Michael Burke

Polyhedral+Dataflow Graphs , Eddie C. Davis

Improving Spellchecking for Children: Correction and Design , Brody Downs

A Collection of Fast Algorithms for Scalar and Vector-Valued Data on Irregular Domains: Spherical Harmonic Analysis, Divergence-Free/Curl-Free Radial Basis Functions, and Implicit Surface Reconstruction , Kathryn Primrose Drake

Privacy-Preserving Protocol for Atomic Swap Between Blockchains , Kiran Gurung

Unsupervised Structural Graph Node Representation Learning , Mikel Joaristi

Detecting Undisclosed Paid Editing in Wikipedia , Nikesh Joshi

Do You Feel Me?: Learning Language from Humans with Robot Emotional Displays , David McNeill

Obtaining Real-World Benchmark Programs from Open-Source Repositories Through Abstract-Semantics Preserving Transformations , Maria Anne Rachel Paquin

Content Based Image Retrieval (CBIR) for Brand Logos , Enjal Parajuli

A Resilience Metric for Modern Power Distribution Systems , Tyler Bennett Phillips

Theses/Dissertations from 2019 2019

Edge-Assisted Workload-Aware Image Processing System , Anil Acharya

MINOS: Unsupervised Netflow-Based Detection of Infected and Attacked Hosts, and Attack Time in Large Networks , Mousume Bhowmick

Deviant: A Mutation Testing Tool for Solidity Smart Contracts , Patrick Chapman

Querying Over Encrypted Databases in a Cloud Environment , Jake Douglas

A Hybrid Model to Detect Fake News , Indhumathi Gurunathan

Suitability of Finite State Automata to Model String Constraints in Probablistic Symbolic Execution , Andrew Harris

UNICORN Framework: A User-Centric Approach Toward Formal Verification of Privacy Norms , Rezvan Joshaghani

Detection and Countermeasure of Saturation Attacks in Software-Defined Networks , Samer Yousef Khamaiseh

Secure Two-Party Protocol for Privacy-Preserving Classification via Differential Privacy , Manish Kumar

Application-Specific Memory Subsystem Benchmarking , Mahesh Lakshminarasimhan

Multilingual Information Retrieval: A Representation Building Perspective , Ion Madrazo

Improved Study of Side-Channel Attacks Using Recurrent Neural Networks , Muhammad Abu Naser Rony Chowdhury

Investigating the Effects of Social and Temporal Dynamics in Fitness Games on Children's Physical Activity , Ankita Samariya

BullyNet: Unmasking Cyberbullies on Social Networks , Aparna Sankaran

FALCON: Framework for Anomaly Detection In Industrial Control Systems , Subin Sapkota

Investigating Semantic Properties of Images Generated from Natural Language Using Neural Networks , Samuel Ward Schrader

Incremental Processing for Improving Conversational Grounding in a Chatbot , Aprajita Shukla

Estimating Error and Bias of Offline Recommender System Evaluation Results , Mucun Tian

Theses/Dissertations from 2018 2018

Leveraging Tiled Display for Big Data Visualization Using D3.js , Ujjwal Acharya

Fostering the Retrieval of Suitable Web Resources in Response to Children's Educational Search Tasks , Oghenemaro Deborah Anuyah

Privacy-Preserving Genomic Data Publishing via Differential Privacy , Tanya Khatri

Injecting Control Commands Through Sensory Channel: Attack and Defense , Farhad Rasapour

Strong Mutation-Based Test Generation of XACML Policies , Roshan Shrestha

Performance, Scalability, and Robustness in Distributed File Tree Copy , Christopher Robert Sutton

Using DNA For Data Storage: Encoding and Decoding Algorithm Development , Kelsey Suyehira

Detecting Saliency by Combining Speech and Object Detection in Indoor Environments , Kiran Thapa

Theses/Dissertations from 2017 2017

Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews , Haritha Akella

Editing Behavior Analysis and Prediction of Active/Inactive Users in Wikipedia , Harish Arelli

CloudSkulk: Design of a Nested Virtual Machine Based Rootkit-in-the-Middle Attack , Joseph Anthony Connelly

Predicting Friendship Strength in Facebook , Nitish Dhakal

Privacy-Preserving Trajectory Data Publishing via Differential Privacy , Ishita Dwivedi

Cultivating Community Interactions in Citizen Science: Connecting People to Each Other and the Environment , Bret Allen Finley

Uncovering New Links Through Interaction Duration , Laxmi Amulya Gundala

Variance: Secure Two-Party Protocol for Solving Yao's Millionaires' Problem in Bitcoin , Joshua Holmes

A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases , Akshay Kansal

Integrity Coded Databases: Ensuring Correctness and Freshness of Outsourced Databases , Ujwal Karki

Editable View Optimized Tone Mapping For Viewing High Dynamic Range Panoramas On Head Mounted Display , Yuan Li

The Effects of Pair-Programming in a High School Introductory Computer Science Class , Ken Manship

Towards Automatic Repair of XACML Policies , Shuai Peng

Identification of Unknown Landscape Types Using CNN Transfer Learning , Ashish Sharma

Hand Gesture Recognition for Sign Language Transcription , Iker Vazquez Lopez

Learning to Code Music : Development of a Supplemental Unit for High School Computer Science , Kelsey Wright

Theses/Dissertations from 2016 2016

Identification of Small Endogenous Viral Elements within Host Genomes , Edward C. Davis Jr.

When the System Becomes Your Personal Docent: Curated Book Recommendations , Nevena Dragovic

Security Testing with Misuse Case Modeling , Samer Yousef Khamaiseh

Estimating Length Statistics of Aggregate Fried Potato Product via Electromagnetic Radiation Attenuation , Jesse Lovitt

Towards Multipurpose Readability Assessment , Ion Madrazo

Evaluation of Topic Models for Content-Based Popularity Prediction on Social Microblogs , Axel Magnuson

CEST: City Event Summarization using Twitter , Deepa Mallela

Developing an ABAC-Based Grant Proposal Workflow Management System , Milson Munakami

Phoenix and Hive as Alternatives to RDBMS , Diana Ornelas

Massively Parallel Algorithm for Solving the Eikonal Equation on Multiple Accelerator Platforms , Anup Shrestha

A Certificateless One-Way Group Key Agreement Protocol for Point-to-Point Email Encryption , Srisarguru Sridhar

Dynamic Machine Level Resource Allocation to Improve Tasking Performance Across Multiple Processes , Richard Walter Thatcher

Theses/Dissertations from 2015 2015

Developing an Application for Evolutionary Search for Computational Models of Cellular Development , Nicolas Scott Cornia

Accelerated Radar Signal Processing in Large Geophysical Datasets , Ravi Preesha Geetha

Integrity Coded Databases (ICDB) – Protecting Integrity for Outsourced Databases , Archana Nanjundarao

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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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Top 10 Best PhD in Computer Science Programs

Are you looking to further your studies in computer science? Perfect! I’ve researched and put together these ten computer science PhD programs, so keep reading to find your next course!

As you know, PhD in computer science holders are sought-after specialists, with current demand far outstripping supply. According to the Bureau for Labor Statistics , jobs for computer and information research scientists are estimated to grow by an impressive 22% between 2020 and 2030. You might score similar positions with a PhD in mathematics , too. But a PhD in computer science can open doors to some of the most lucrative jobs in the industry.

Let’s explore some of the best computer science PhD courses in the US!

Table of Contents

Best Computer Science PhD Programs and Universities

Stanford university.

PhD in Computer Science

Stanford University is one of the most famous research institutions in the world, and its Computer Science programs have been ranked second  in the USA. This PhD program involves a research rotation scheme where students participate in different research groups during the first year. This exposes them to different subjects and lets them know the faculty and fellow students.

• Courses : Analysis of algorithms, programming languages, and computer network & security.
• Credits : 135 course units
• Duration : 5-6 years
• Delivery : On-campus
• Tuition : Refer tuition page  (full funding available)
• Financial aid : Research assistantships, teaching assistantships, fellowships, and grants
• Acceptance rate:  5.2%
• Location : Stanford, California

Massachusetts Institute of Technology

PhD in Computer Science and Engineering

MIT is known as the best technical institution in the world, and its computer science programs were ranked first  in the country by the US & News report. Students can work with all schools and departments throughout their studies.

• Courses : Software & computation for simulation, process data analytics & machine learning, and numerical computing & interactive software.
• Credits : 60
• Tuition : Refer tuition page
• Financial aid:  Scholarships, federal work-study, fellowships, assistantships, grants, and veteran benefits
• Acceptance rate:  7.3%
• Location : Cambridge, Massachusetts

Carnegie Mellon University, School of Computer Sciences

CMU is a globally acclaimed private research university, home to conducting cutting-edge technology research across its seven prestigious schools. This is one of the few PhD programs in computer science with an optional dual degree arrangement enabling you to study programs with one of seven Portuguese universities.

• Courses : Algorithms & complexity, artificial intelligence, and software systems.
• Credits : 96 university units
• Tuition : $48,250 per year
• Financial aid:  Full funding, fellowships, and scholarships
• Acceptance rate : 17.3%
• Location : Pittsburgh, Pennsylvania

Harvard University, Harvard John A. Paulson School of Engineering and Applied Sciences

Harvard University is a world-renowned research institution that aims to achieve the perfect mix of scholarship and innovation. Across the university, every PhD student is given a field advisor right from the beginning of the program. Then, the student identifies a research area and a potential research advisor in their first two semesters. Upon qualifying, the research advisor nominates a research committee to assist the student throughout their graduate career.

• Courses : Algorithms & their limitations, data structures & algorithms, and cryptography.
• Credits : 16 half-courses
• Duration : 2 years minimum
• Financial aid : Full funding, fellowships, teaching assistantships, and research assistantships
• Acceptance rate:  5%

Duke University, The Graduate School

Duke University is an internationally acclaimed private research university known for its inclusivity. Its Center for Exemplary Mentoring aims to increase the number of PhD graduates from underrepresented and minority communities. Its PhD of Computer Science program is flexible and allows students to choose between a coursework-only option of 30 credits or a thesis.

• Courses : Programming & problem solving, computational microeconomics, and software design & implementation.
• Credits : 8 courses
• Duration : 5 years
• Tuition : $4,325  per semester
• Financial aid : Grants, fellowships, teaching assistantships, and research assistantships
• Acceptance rate : 7.7%
• Location : Durham, North Carolina

The University of California Berkeley, Department of Electrical Engineering and Computer Sciences (EECS)

The University of California Berkeley is a prestigious university committed to student diversity and has a dedicated Office for Graduate Diversity to support students from all backgrounds. This PhD program offers research opportunities in biosystems and computational biology, cyber-physical systems and design automation (CPSDA), and computer architecture and engineering.

• Courses : Combinatorial algorithms & data structures, design of programming languages, and implementation of database systems.
• Credits : 24 units minimum
• Duration : 5.5 – 6 years
• Tuition : $6,132 per semester
• Financial aid : Fellowships, scholarships, grants, research stipends, loans, and work-study
• Acceptance rate : 17%
• Location : Berkeley, California

California Institute of Technology, Computing & Mathematical Sciences Department

The California Institute of Technology, also known as Caltech, is one of the most renowned technology institutions in the world despite its comparatively small size. This PhD program allows students to develop an in-depth understanding of and conduct research in areas related to mathematical and algorithmic foundations of computer science.

• Courses : Quantum cryptography, information theory, and network control systems.
• Credits : 135 units
• Duration : 6 years
• Tuition : $63,063  per year
• Financial aid : Fellowships, assistantships, loans, stipends, scholarships, and work-study
• Acceptance rate : 6.7%
• Location : Pasadena, California

Cornell University, Bowers College of Computing and Information Science

Cornell University is home to several famous technology schools, such as Cornell Tech, which conducts cutting-edge research to find solutions to the challenges of our modern, digital world. In this PhD program, students choose a minor from one of the 90 available fields outside of computer science to widen their knowledge base.

• Courses : Parallel computing, programming environments, and natural language processing.
• Duration : 12 semesters
• Tuition : $24,800  per year
• Financial aid:  Assistantships, fellowships, loans, and stipends
• Acceptance rate : 10.7%
• Location : Ithaca, New York

The University of Illinois Urbana-Champaign, The Grainger College of Engineering

The Grainger College of Engineering focuses on research to improve quality of life through innovation, entrepreneurship, and societal engagement. In this flexible PhD program, students are assigned three committee members during the first semester. The student can then consult with these mentors to plan their studies to meet their career goals.

• Courses : Programming language semantics, machine learning for signals, and learning-based robotics.
• Credits : 96, or 64 if you already have an MS
• Duration : 5-7 years
• Financial aid : Grants, fellowships, waivers, loans, and employment
• Acceptance rate : 63.3%
• Location : Urbana, Illinois

Princeton University, The Graduate School

Princeton University is one of the top universities in the country in computer science doctorate programs. This PhD program involves studying six courses, including one each from the three main areas of Artificial Intelligence, Systems, and Theory, which form the program’s core.

• Courses : Programming languages, advanced computer systems, and information theory & applications.
• Credits : 6 courses
• Tuition : $62,860  per year
• Financial aid : Teaching assistantships, research assistantships, and fellowships
• Acceptance rate : 5.6%
• Location : Princeton, New Jersey

What Do I Need to Get a PhD in Computer Science?

For most programs, you’ll need a bachelor’s or master’s degree in computer science or a related field; however, exact eligibility requirements vary depending on the school. To earn your PhD, you’ll typically need to complete coursework, qualifying exams, and a dissertation.

What to Consider When Choosing a Computer Science PhD Program

Several US schools and universities offer PhD in computer science programs — choosing the right program for you can feel overwhelming. So, take your time and research the curriculum and specialties for different programs to make sure they match your areas of interest.

If you’re unsure about the areas you want to specialize in, I advise you to read research papers across different fields and discuss career opportunities with people in the industry. It’s also a good idea to look up the faculty from the programs you’re interested in and review their recent papers.

Here are some key factors to keep in mind when choosing a computer science PhD:

• Curriculum and specialties offered
• The program’s reputation
• Faculty, their specialties, and reputation
• Cost of tuition and other fees
• Delivery mode: on-campus, online, or hybrid
• Funding options

Related Reading:  Top 10 Best Online PhDs in Computer Science

Why Get a Doctor of Computer Science Degree?

A doctorate in computer science will equip you to become a leading researcher in today’s digital technologies. You’ll also be eligible for senior academic positions with a PhD in the field.

Furthermore, a PhD in computer science allows you to work in various respectable roles. Here are some of the top jobs and average yearly salaries for computer science PhD holders:

• Data Scientist – $99,710
• Chief Data Scientist –  $211,702
• Software Architect – $136,541
• Software Development Manager – $133,534
• Senior Researcher – $107,657

Approximately 2,000 students earn a computer science PhD from US universities each year. The industry demand is much greater than the supply. Furthermore, many PhD candidates are international students who return home after graduation. Therefore, graduates with a PhD in computer science are in high demand in the US.

PhD in Computer Science: Key Facts

How much does a phd in computer science cost.

Tuition varies depending on the program but generally falls between $15,000 and $60,000 per year. Besides the tuition fees, you will also need to factor in additional costs like academic fees, books, other educational resources, and living expenses.

How Long Does It Take to Get a PhD in Computer Science?

A PhD in computer science usually takes 3-7 years to complete.

What Skills Do You Gain from Doing a PhD in Computer Science?

A PhD in computer science allows students to develop leadership, problem-solving, and research skills related to complex topics like artificial intelligence, machine learning, and robotics.

Key Takeaways

A PhD in computer science is one of the most in-demand qualifications in today’s hyper-digital world. It can equip you with specialized skills and knowledge to address modern tech problems with innovative solutions.

If you found this article helpful, take a look at our other guides, including the best Master’s in Computer Science programs , the top online PhD programs , and the highest paying PhDs .

Frequently Asked Questions

What is a phd in computer science like.

A PhD in computer science typically involves research, coursework, thesis preparation, teaching, and seminars related to computer science subjects. Individual programs may have other requirements.

Can You Get a PhD in Computer Science?

Yes, many universities offer computer science PhD programs . You’ll usually need a bachelor’s or a master’s degree in computer science or a related field to apply.

Is a PhD in Computer Science Worth It?

While a PhD in computer science requires considerable financial investment, it is a valuable qualification in today’s tech-forward world. Plenty of job opportunities and high remuneration levels await a computer science PhD graduate, with the average annual salary of $133,000 per year.

What Does a PhD in Computer Science Do?

A qualified PhD computer science graduate may conduct research in complex computer systems, design and develop programs and applications, or study human-computer interaction to find innovative solutions for society. They may also work in academics, either in teaching, research, or both.

What Can I Do with a PhD in Computer Science?

With a PhD in Computer Science on your resume, you’ll be hired for roles like data scientist, software architect, researcher, or academic professor and build a prestigious, high-paid career.

Which Subject is Best for a PhD in Computer Science?

A wide range of subjects will qualify you to apply for a doctoral degree in computer science. Popular topics include data structures and algorithms, computer systems and organization, and discrete computer science mathematics. The best subject for you depends on your interests and career ambitions.

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Lisa is a full-time writer specializing in career advice, further education, and personal development. She works from all over the world, and when not writing you'll find her hiking, practicing yoga, or enjoying a glass of Malbec.

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• Where To Earn A Ph.D. In Computer Science Online

Where To Earn A Ph.D. In Computer Science Online In 2024

Published: Mar 27, 2024, 3:00pm

As our reliance on technology grows, so does our need for advanced computer professionals and educators. Despite the number of conferred graduate degrees in computer and information sciences nearly tripling between the 2010–11 and 2020–21 academic years, tech fields are facing a widening skills gap.

While many schools nationwide have developed computer science doctoral programs to help meet professional demand, online Ph.D. in computer science programs are still quite rare. Most schools only offer in-person programs, meaning students who need to work while they study have limited options.

In this guide, we showcase the two online doctorates in computer science that met our ranking criteria. We also explore factors you should consider when choosing a program.

Why You Can Trust Forbes Advisor Education

Forbes Advisor’s education editors are committed to producing unbiased rankings and informative articles covering online colleges, tech bootcamps and career paths. Our ranking methodologies use data from the National Center for Education Statistics , education providers, and reputable educational and professional organizations. An advisory board of educators and other subject matter experts reviews and verifies our content to bring you trustworthy, up-to-date information. Advertisers do not influence our rankings or editorial content.

• 6,290 accredited, nonprofit colleges and universities analyzed nationwide
• 52 reputable tech bootcamp providers evaluated for our rankings
• All content is fact-checked and updated on an annual basis
• Rankings undergo five rounds of fact-checking
• Only 7.12% of all colleges, universities and bootcamp providers we consider are awarded
• Best Online Cybersecurity Degrees
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Degree Finder

Online ph.d. in computer science options, how to find the right online ph.d. in computer science for you, should you enroll in an online computer science ph.d. program, accreditation for online computer science ph.d. programs, frequently asked questions (faqs) about earning an online ph.d. in computer science, capitol technology university, national university, featured online schools.

Learn about start dates, transferring credits, availability of financial credit and much more by clicking 'Visit Site'

Maryland-based Capitol Technology University , which neighbors Washington, D.C., offers 41 online doctoral programs, including an online Ph.D. in computer science. Students learn to evaluate and think critically about computer science issues, actions and perspectives.

The degree typically takes two to three years to complete. Students can choose between a dissertation defense track or a publication track. The publication option requires Ph.D. candidates to publish three peer-reviewed articles. Both tracks feature entirely online and asynchronous coursework with no residency requirements.

Capitol Tech’s online doctoral courses cost $950 per credit, regardless of state residency. Active military service members receive a $100 discount per credit, while retired military service members receive a $50 discount. Learners pay an information technology fee of $40 per credit.

• School Type: Private
• Application Fee: $100
• Degree Credit Requirements: 60 credits
• Program Enrollment Options: Full-time
• Notable Major-Specific Courses: Computer science future demands, computer science research background
• Concentrations Available: N/A
• In-Person Requirements: No

Founded in 1971, National University (NU) offers more than 190 online programs. Students enrolled in the online Ph.D. in computer science choose their research topic and method, picking between quantitative, qualitative and constructive research. Learners also complete replication studies and develop portfolios.

With weekly start dates, the online Ph.D. takes just over three years to complete. Except for 12-week dissertation courses, virtual classes last eight weeks. Instructors may deliver classes asynchronously or synchronously. Despite the school’s location in San Diego, California, NU charges all online learners the same tuition. The Ph.D. costs an estimated $58,560.

• Application Fee: Free
• Notable Major-Specific Courses: Data curation, artificial intelligence

Consider Your Future Goals

Your postgraduate goals should play a central role in your school and program decisions. Knowing what type of career and specialization you want can help you choose the right online doctorate in computer science.

For example, you might enroll in a research-based program if you plan on teaching, while a practice-based program may suit you if you aspire to take on an advanced computer science role .

You can also compare how well each school and program supports your plans. Check each degree’s curriculum, faculty, and mentorship and partnership opportunities to identify how it can help you meet your long-term goals.

Understand Your Expenses and Financing Options

The high cost of a graduate degree can make postsecondary education seem out of reach for many. Total tuition for the programs on this list costs $57,000 at Capital Tech and around $59,000 at NU—that’s a hefty financial investment.

However, you may have financial aid available to you. By completing the Free Application for Federal Student Aid (FAFSA®), you can qualify for various federal grants, scholarships and work-study programs. Other aid providers may use FAFSA data to determine their awards as well. Some schools, including Capitol Tech, also offer tuition discounts to veterans and active service members.

For many students, online degrees make graduate school more accessible thanks to flexible scheduling and reduced travel demands. But the online learning experience differs from on-campus programs, and earning a Ph.D. in computer science online might not work for everyone.

Both online programs on this page feature asynchronous courses, allowing you to study on your own time. However, this also requires more independence, time management and organization. You might also find the environment to be less structured and interactive.

If you thrive in a more traditional classroom experience, synchronous online classes or a hybrid program might fit your needs better.

Accreditation ensures your school or program has undergone a rigorous evaluation process. Your university’s institutional accreditation status can affect your eligibility for financial aid, transfer credits, professional credentials and employment.

Check that your college is accredited by an organization approved by the U.S. Department of Education or the Council for Higher Education Accreditation (CHEA). You can confirm any school’s accreditation status through CHEA’s directories .

Programmatic accreditation provides quality assurance for specific degree programs and departments within universities. ABET accredits computer science degrees , but it does not provide accreditation for doctoral programs.

Our Methodology

We ranked two accredited, nonprofit colleges offering online computer science Ph.D. programs in the U.S. using 14 data points in the categories of student experience, credibility, student outcomes and affordability. We pulled data for these categories from reliable resources such as the Integrated Postsecondary Education Data System ; private, third-party data sources; and individual school and program websites.

Data is accurate as of February 2024. Note that because online doctorates are relatively uncommon, fewer schools meet our ranking standards at the doctoral level.

We scored schools based on the following metrics:

Student Experience:

• Student-to-faculty ratio
• Socioeconomic diversity
• Availability of online coursework
• Total number of graduate assistants
• Portion of graduate students enrolled in at least some distance education

Credibility:

• Fully accredited
• Programmatic accreditation status
• Nonprofit status

Student Outcomes:

• Overall graduation rate
• Median earnings 10 years after graduation

Affordability:

• In-state graduate student tuition and fees
• Alternative tuition plans offered
• Median federal student loan debt
• Student loan default rate

We listed the two schools in the U.S. that met our ranking criteria.

Find our full list of methodologies here .

Can I get a Ph.D. in computer science online?

Several schools offer a fully remote or hybrid Ph.D. in computer science. Online degrees typically feature the same content as their in-person counterparts and award the same academic credentials; in many cases, they differ only in delivery format.

Can you get a Ph.D. virtually?

Yes, fully online Ph.D.s in computer science allow you to complete your coursework, perform research, watch seminars and attend meetings virtually. However, your program may feature fieldwork requirements that must be completed in person.

Is it worth getting a Ph.D. in computer science?

A Ph.D. in computer science can prove beneficial for many people, including those who want to work in a postsecondary teaching or research position. Doctorates in computer science can also help professionals advance their careers and take on influential positions in the industry.

How long is a Ph.D. in CS?

The length of your Ph.D. in computer science depends on the program, your previous education and your course load. The programs on this list take about three years to complete, but many others take four to five years.

For nearly a decade, Doug Wintemute has specialized in helping students and professionals make sound education and career decisions. In addition to Forbes Advisor, his work has been featured on many online publications, including ZDNet, Bankrate and NurseJournal.

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• Dissertation

Prize-Winning Thesis and Dissertation Examples

Published on September 9, 2022 by Tegan George . Revised on July 18, 2023.

It can be difficult to know where to start when writing your thesis or dissertation . One way to come up with some ideas or maybe even combat writer’s block is to check out previous work done by other students on a similar thesis or dissertation topic to yours.

This article collects a list of undergraduate, master’s, and PhD theses and dissertations that have won prizes for their high-quality research.

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Award-winning undergraduate theses, award-winning master’s theses, award-winning ph.d. dissertations, other interesting articles.

University : University of Pennsylvania Faculty : History Author : Suchait Kahlon Award : 2021 Hilary Conroy Prize for Best Honors Thesis in World History Title : “Abolition, Africans, and Abstraction: the Influence of the “Noble Savage” on British and French Antislavery Thought, 1787-1807”

University : Columbia University Faculty : History Author : Julien Saint Reiman Award : 2018 Charles A. Beard Senior Thesis Prize Title : “A Starving Man Helping Another Starving Man”: UNRRA, India, and the Genesis of Global Relief, 1943-1947

University: University College London Faculty: Geography Author: Anna Knowles-Smith Award:  2017 Royal Geographical Society Undergraduate Dissertation Prize Title:  Refugees and theatre: an exploration of the basis of self-representation

University: University of Washington Faculty:  Computer Science & Engineering Author: Nick J. Martindell Award: 2014 Best Senior Thesis Award Title:  DCDN: Distributed content delivery for the modern web

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University:  University of Edinburgh Faculty:  Informatics Author:  Christopher Sipola Award:  2018 Social Responsibility & Sustainability Dissertation Prize Title:  Summarizing electricity usage with a neural network

University:  University of Ottawa Faculty:  Education Author:  Matthew Brillinger Award:  2017 Commission on Graduate Studies in the Humanities Prize Title:  Educational Park Planning in Berkeley, California, 1965-1968

University:  University of Ottawa Faculty: Social Sciences Author:  Heather Martin Award:  2015 Joseph De Koninck Prize Title:  An Analysis of Sexual Assault Support Services for Women who have a Developmental Disability

University : University of Ottawa Faculty : Physics Author : Guillaume Thekkadath Award : 2017 Commission on Graduate Studies in the Sciences Prize Title : Joint measurements of complementary properties of quantum systems

University:  London School of Economics Faculty: International Development Author: Lajos Kossuth Award:  2016 Winner of the Prize for Best Overall Performance Title:  Shiny Happy People: A study of the effects income relative to a reference group exerts on life satisfaction

University : Stanford University Faculty : English Author : Nathan Wainstein Award : 2021 Alden Prize Title : “Unformed Art: Bad Writing in the Modernist Novel”

University : University of Massachusetts at Amherst Faculty : Molecular and Cellular Biology Author : Nils Pilotte Award : 2021 Byron Prize for Best Ph.D. Dissertation Title : “Improved Molecular Diagnostics for Soil-Transmitted Molecular Diagnostics for Soil-Transmitted Helminths”

University:  Utrecht University Faculty:  Linguistics Author:  Hans Rutger Bosker Award: 2014 AVT/Anéla Dissertation Prize Title:  The processing and evaluation of fluency in native and non-native speech

University: California Institute of Technology Faculty: Physics Author: Michael P. Mendenhall Award: 2015 Dissertation Award in Nuclear Physics Title: Measurement of the neutron beta decay asymmetry using ultracold neutrons

University:  Stanford University Faculty: Management Science and Engineering Author:  Shayan O. Gharan Award:  Doctoral Dissertation Award 2013 Title:   New Rounding Techniques for the Design and Analysis of Approximation Algorithms

University: University of Minnesota Faculty: Chemical Engineering Author: Eric A. Vandre Award:  2014 Andreas Acrivos Dissertation Award in Fluid Dynamics Title: Onset of Dynamics Wetting Failure: The Mechanics of High-speed Fluid Displacement

University: Erasmus University Rotterdam Faculty: Marketing Author: Ezgi Akpinar Award: McKinsey Marketing Dissertation Award 2014 Title: Consumer Information Sharing: Understanding Psychological Drivers of Social Transmission

University: University of Washington Faculty: Computer Science & Engineering Author: Keith N. Snavely Award:  2009 Doctoral Dissertation Award Title: Scene Reconstruction and Visualization from Internet Photo Collections

University:  University of Ottawa Faculty:  Social Work Author:  Susannah Taylor Award: 2018 Joseph De Koninck Prize Title:  Effacing and Obscuring Autonomy: the Effects of Structural Violence on the Transition to Adulthood of Street Involved Youth

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• Programming

Latest Computer Science Research Topics for 2024

Home Blog Programming Latest Computer Science Research Topics for 2024

Everybody sees a dream—aspiring to become a doctor, astronaut, or anything that fits your imagination. If you were someone who had a keen interest in looking for answers and knowing the “why” behind things, you might be a good fit for research. Further, if this interest revolved around computers and tech, you would be an excellent computer researcher!

As a tech enthusiast, you must know how technology is making our life easy and comfortable. With a single click, Google can get you answers to your silliest query or let you know the best restaurants around you. Do you know what generates that answer? Want to learn about the science going on behind these gadgets and the internet?

For this, you will have to do a bit of research. Here we will learn about top computer science thesis topics and computer science thesis ideas.

Why is Research in Computer Science Important?

Computers and technology are becoming an integral part of our lives. We are dependent on them for most of our work. With the changing lifestyle and needs of the people, continuous research in this sector is required to ease human work. However, you need to be a certified researcher to contribute to the field of computers. You can check out Advance Computer Programming certification to learn and advance in the versatile language and get hands-on experience with all the topics of C# application development.

1. Innovation in Technology

Research in computer science contributes to technological advancement and innovations. We end up discovering new things and introducing them to the world. Through research, scientists and engineers can create new hardware, software, and algorithms that improve the functionality, performance, and usability of computers and other digital devices.

2. Problem-Solving Capabilities

From disease outbreaks to climate change, solving complex problems requires the use of advanced computer models and algorithms. Computer science research enables scholars to create methods and tools that can help in resolving these challenging issues in a blink of an eye.

3. Enhancing Human Life

Computer science research has the potential to significantly enhance human life in a variety of ways. For instance, researchers can produce educational software that enhances student learning or new healthcare technology that improves clinical results. If you wish to do Ph.D., these can become interesting computer science research topics for a PhD.

4. Security Assurance

As more sensitive data is being transmitted and kept online, security is our main concern. Computer science research is crucial for creating new security systems and tactics that defend against online threats.

Top Computer Science Research Topics

Before starting with the research, knowing the trendy research paper ideas for computer science exploration is important. It is not so easy to get your hands on the best research topics for computer science; spend some time and read about the following mind-boggling ideas before selecting one.

1. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues, and Challenges

Welcome to the era of seamless connectivity and unparalleled efficiency! Blockchain and edge computing are two cutting-edge technologies that have the potential to revolutionize numerous sectors. Blockchain is a distributed ledger technology that is decentralized and offers a safe and transparent method of storing and transferring data.

As a young researcher, you can pave the way for a more secure, efficient, and scalable architecture that integrates blockchain and edge computing systems. So, let's roll up our sleeves and get ready to push the boundaries of technology with this exciting innovation!

Blockchain helps to reduce latency and boost speed. Edge computing, on the other hand, entails processing data close to the generation source, such as sensors and IoT devices. Integrating edge computing with blockchain technologies can help to achieve safer, more effective, and scalable architecture.

Moreover, this research title for computer science might open doors of opportunities for you in the financial sector.

2. A Survey on Edge Computing Systems and Tools

With the rise in population, the data is multiplying by manifolds each day. It's high time we find efficient technology to store it. However, more research is required for the same.

Say hello to the future of computing with edge computing! The edge computing system can store vast amounts of data to retrieve in the future. It also provides fast access to information in need. It maintains computing resources from the cloud and data centers while processing.

Edge computing systems bring processing power closer to the data source, resulting in faster and more efficient computing. But what tools are available to help us harness the power of edge computing?

As a part of this research, you will look at the newest edge computing tools and technologies to see how they can improve your computing experience. Here are some of the tools you might get familiar with upon completion of this research:

• Apache NiFi:  A framework for data processing that enables users to gather, transform, and transfer data from edge devices to cloud computing infrastructure.
• Microsoft Azure IoT Edge: A platform in the cloud that enables the creation and deployment of cutting-edge intelligent applications.
• OpenFog Consortium:  An organization that supports the advancement of fog computing technologies and architectures is the OpenFog Consortium.

3. Machine Learning: Algorithms, Real-world Applications, and Research Directions

Machine learning is the superset of Artificial Intelligence; a ground-breaking technology used to train machines to mimic human action and work. ML is used in everything from virtual assistants to self-driving cars and is revolutionizing the way we interact with computers. But what is machine learning exactly, and what are some of its practical uses and future research directions?

To find answers to such questions, it can be a wonderful choice to pick from the pool of various computer science dissertation ideas.

You will discover how computers learn several actions without explicit programming and see how they perform beyond their current capabilities. However, to understand better, having some basic programming knowledge always helps. KnowledgeHut’s Programming course for beginners will help you learn the most in-demand programming languages and technologies with hands-on projects.

During the research, you will work on and study

• Algorithm: Machine learning includes many algorithms, from decision trees to neural networks.
• Applications in the Real-world: You can see the usage of ML in many places. It can early detect and diagnose diseases like cancer. It can detect fraud when you are making payments. You can also use it for personalized advertising.
• Research Trend:  The most recent developments in machine learning research, include explainable AI, reinforcement learning, and federated learning.

While a single research paper is not enough to bring the light on an entire domain as vast as machine learning; it can help you witness how applicable it is in numerous fields, like engineering, data science & analysis, business intelligence, and many more.

Whether you are a data scientist with years of experience or a curious tech enthusiast, machine learning is an intriguing and vital field that's influencing the direction of technology. So why not dig deeper?

4. Evolutionary Algorithms and their Applications to Engineering Problems

Imagine a system that can solve most of your complex queries. Are you interested to know how these systems work? It is because of some algorithms. But what are they, and how do they work? Evolutionary algorithms use genetic operators like mutation and crossover to build new generations of solutions rather than starting from scratch.

This research topic can be a choice of interest for someone who wants to learn more about algorithms and their vitality in engineering.

Evolutionary algorithms are transforming the way we approach engineering challenges by allowing us to explore enormous solution areas and optimize complex systems.

The possibilities are infinite as long as this technology is developed further. Get ready to explore the fascinating world of evolutionary algorithms and their applications in addressing engineering issues.

5. The Role of Big Data Analytics in the Industrial Internet of Things

Datasets can have answers to most of your questions. With good research and approach, analyzing this data can bring magical results. Welcome to the world of data-driven insights! Big Data Analytics is the transformative process of extracting valuable knowledge and patterns from vast and complex datasets, boosting innovation and informed decision-making.

This field allows you to transform the enormous amounts of data produced by IoT devices into insightful knowledge that has the potential to change how large-scale industries work. It's like having a crystal ball that can foretell.

Big data analytics is being utilized to address some of the most critical issues, from supply chain optimization to predictive maintenance. Using it, you can find patterns, spot abnormalities, and make data-driven decisions that increase effectiveness and lower costs for several industrial operations by analyzing data from sensors and other IoT devices.

The area is so vast that you'll need proper research to use and interpret all this information. Choose this as your computer research topic to discover big data analytics' most compelling applications and benefits. You will see that a significant portion of industrial IoT technology demands the study of interconnected systems, and there's nothing more suitable than extensive data analysis.

6. An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy

Are you concerned about the security and privacy of your Internet of Things (IoT) devices? As more and more devices become connected, it is more important than ever to protect the security and privacy of data. If you are interested in cyber security and want to find new ways of strengthening it, this is the field for you.

ELIB is a cutting-edge solution that offers private and secure communication between IoT devices by fusing the strength of blockchain with lightweight cryptography. This architecture stores encrypted data on a distributed ledger so only parties with permission can access it.

But why is ELIB so practical and portable? ELIB uses lightweight cryptography to provide quick and effective communication between devices, unlike conventional blockchain models that need complicated and resource-intensive computations.

Due to its increasing vitality, it is gaining popularity as a research topic as someone aware that this framework works and helps reinstate data security is highly demanded in financial and banking.

7. Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research Topics

Welcome to the world where machines decode the beauty of the human language. With natural language processing (NLP) techniques, we can analyze the interactions between humans and computers to reveal valuable insights for development research topics. It is also one of the most crucial PhD topics in computer science as NLP-based applications are gaining more and more traction.

Etymologically, natural language processing (NLP) is a potential technique that enables us to examine and comprehend natural language data, such as discussions between people and machines. Insights on user behaviour, preferences, and pain areas can be gleaned from these encounters utilizing NLP approaches.

But which specific areas should we leverage on using NLP methods? This is precisely what you’ll discover while doing this computer science research.

Gear up to learn more about the fascinating field of NLP and how it can change how we design and interact with technology, whether you are a UX designer, a data scientist, or just a curious tech lover and linguist.

8. All One Needs to Know About Fog Computing and Related Edge Computing Paradigms: A Complete Survey

If you are an IoT expert or a keen lover of the Internet of Things, you should leap and move forward to discovering Fog Computing. With the rise of connected devices and the Internet of Things (IoT), traditional cloud computing models are no longer enough. That's where fog computing and related edge computing paradigms come in.

Fog computing is a distributed approach that brings processing and data storage closer to the devices that generate and consume data by extending cloud computing to the network's edge.

As computing technologies are significantly used today, the area has become a hub for researchers to delve deeper into the underlying concepts and devise more and more fog computing frameworks. You can also contribute to and master this architecture by opting for this stand-out topic for your research.

Tips and Tricks to Write Computer Research Topics

Before starting to explore these hot research topics in computer science you may have to know about some tips and tricks that can easily help you.

• Know your interest.
• Choose the topic wisely.
• Make proper research about the demand of the topic.
• Get proper references.
• Discuss with experts.

By following these tips and tricks, you can write a compelling and impactful computer research topic that contributes to the field's advancement and addresses important research gaps.

From machine learning and artificial intelligence to blockchain, edge computing, and big data analytics, numerous trending computer research topics exist to explore.

One of the most important trends is using cutting-edge technology to address current issues. For instance, new IIoT security and privacy opportunities are emerging by integrating blockchain and edge computing. Similarly, the application of natural language processing methods is assisting in revealing human-computer interaction and guiding the creation of new technologies.

Another trend is the growing emphasis on sustainability and moral considerations in technological development. Researchers are looking into how computer science might help in innovation.

With the latest developments and leveraging cutting-edge tools and techniques, researchers can make meaningful contributions to the field and help shape the future of technology. Going for Full-stack Developer online training will help you master the latest tools and technologies. 

Frequently Asked Questions (FAQs)

Research in computer science is mainly focused on different niches. It can be theoretical or technical as well. It completely depends upon the candidate and his focused area. They may do research for inventing new algorithms or many more to get advanced responses in that field.  

Yes, moreover it would be a very good opportunity for the candidate. Because computer science students may have a piece of knowledge about the topic previously. They may find Easy thesis topics for computer science to fulfill their research through KnowledgeHut. 

 There are several scopes available for computer science. A candidate can choose different subjects such as AI, database management, software design, graphics, and many more. 

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Undergraduate research is often done through CURIS, for academic credit, or through an informal arrangement with a professor.

Independent Study Research Courses

• CS191 or CS191W . These courses are each a one-quarter research project that fulfills the Senior Project requirement. CS191W is the Writing in the Major (WiM) version of this course. Students must set up their project, find a faculty sponsor, and submit a proposal.
• CS195.  These courses are one-quarter research project that fulfills a CS elective on the undergraduate program sheet (up to four units) for students who are not yet eligible to take CS191. Students must work under faculty supervision and get prior approval for their project.
• CS199 (Bachelor's) and CS399 (Master's). Are a one-quarter independent studies for students who have already taken four units of CS195 but are not eligible for CS191. These units will count towards the University-wide graduation requirements, but not toward the CS major itself.

For all of these classes, students can propose a project of their own or can find and receive approval for a project listed on the  undergraduate CS research  website. All students must get prior approval from their faculty sponsor, at the very least, before signing up for any of these classes.

Research Project Course

• CS294 or CS294W . These courses fulfill the Senior Project requirement and allow students to get involved in a major ongoing research project. CS294W is the Writing in the Major (WiM) version of the course.
• Note : Check the course listing to see which research areas are covered in 294 for any given year.

Research Course Flowchart

Still not sure which course is suitable for you? Consider this flow chart:

Janani Sundaresan receives 2024 Faculty of Mathematics Graduate Research Excellence Award

Janani Sundaresan , a PhD candidate at the Cheriton School of Computer Science, has been awarded a Faculty of Mathematics Graduate Research Excellence Award . Conferred annually to two graduate students who have authored or coauthored an outstanding research paper, the prestigious recognition comes with a prize of $5,000.

Janani’s paper titled “ Hidden Permutations to the Rescue: Multi-Pass Semi-Streaming Lower Bounds for Approximate Matchings ” was co-authored with her doctoral advisor Professor Sepehr Assadi . It was presented at FOCS 2023 , the 64 th IEEE Symposium on Foundations of Computer Science, one of the two top international conferences in theoretical computer science.

Left to right: Janani Sundaresan and Sepehr Assadi Janani Sundaresan is a PhD student in the computer science theory group at the Cheriton School of Computer Science. She is interested in sublinear and streaming algorithms, online algorithms and more broadly optimization problems. Recently, she has focused on proving lower bounds for streaming algorithms. Sepehr Assadi is an Associate Professor and a Faculty of Mathematics Research Chair at the Cheriton School of Computer Science. His research interest is in theoretical computer science and primarily algorithm design and complexity theory for modern models of computation. This includes sublinear algorithms and lower bounds in various models for processing massive datasets such as streaming, distributed, massively parallel, and sublinear time algorithms. He is also interested in graph theory, communication complexity, online algorithms, and algorithmic game theory.

Janani’s paper makes substantial progress on a longstanding open question regarding the maximum matching problem in the semi-streaming model. The maximum matching problem is the problem of finding the largest number of edges in a given graph that do not share any vertices. As an application, consider finding a pairing of a set of items to a set of interested buyers in a way that each buyer receives at most one item and each item is allocated to at most one buyer. This application can be modelled as an instance of the maximum matching problem on a graph between items and buyers, with edges showing which buyer is interested in which item. The maximum matching problem has been a cornerstone of algorithmic research for almost a century, including pioneering work by Jack Edmonds and William Tutte, mathematicians both formerly affiliated with the University of Waterloo.

The semi-streaming model is a model of computation tailored toward processing massive graphs.  In the classical view of algorithms, often referred to as the von Neumann model, we assume that our algorithms have random-access to every part of the input at a unit cost. This model, however, can be unrealistic when working with inputs that are too large to be stored in the main memory of the computer. Instead, in such scenarios, one typically only has sequential access to the input — say, stored in the hard-drive of the computer which allows a fast sequential access but a slow random access — and needs to process it with a memory much smaller than the input size — say, corresponding to the main memory of the computer, which is typically much smaller than its hard-drive. The semi-streaming model precisely captures this scenario. Specifically, a semi-streaming algorithm needs to process its input by making one or a few passes over the edges of the input graph and use a limited memory, proportional only to the number of vertices in the graph as opposed to the potentially much larger number of edges.

Janani’s paper now considers the following question: does finding even a near-optimal maximum matching in the semi-streaming model require making a substantial number of passes over the input? Stated more formally, the paper asks how many passes are needed to find a (1 + ϵ)-approximation of maximum matching as a function of ϵ.

Given the central role of the maximum matching problem in algorithm design and its wide range of applications, this problem has received significant attention since the introduction of the semi-streaming model almost two decades ago. Yet, almost no progress has been made on the open question except for one- or two-pass algorithms. Janani’s paper, on the other hand, proves the following result:

Any semi-streaming algorithm for finding a (1+ϵ)-approximation of maximum matching requires Ω (log (1/ϵ)) passes, even for ϵ = θ(1). The lower bound holds assuming a natural combinatorial hypothesis regarding the density of the so-called Ruzsa-Szemeredi graphs in extremal graph theory. 

Consequently, while we might still not know the optimal pass-dependence on the parameter ϵ, this is the first time that we have any evidence toward necessity of any dependence at all on this parameter, Professor Assadi explained.

“To put this result in perspective, before Janani’s work, we only knew that maximum matching cannot be solved in one or two passes over the input,” Professor Assadi said. “Thus, it was entirely conceivable that this problem could have been solved by making, say, just three passes over the input, regardless of how accurate an approximate solution we aimed for. On the other hand, Janani’s result now proves that, under a natural combinatorial hypothesis, no constant number of passes, say, even a hundred or a thousand, suffices for solving this problem, as long as we require a sufficiently accurate solution.” 

To learn more about this award-winning research, please see Sepehr Assadi and Janani Sundaresan, “ Hidden Permutations to the Rescue: Multi-Pass Streaming Lower Bounds for Approximate Matchings ,” 2023 IEEE 64 th Annual Symposium on Foundations of Computer Science (FOCS) , Santa Cruz, CA, USA, 2023, pp. 909–932.

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Title: grm: large gaussian reconstruction model for efficient 3d reconstruction and generation.

Abstract: We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from sparse-view images in around 0.1s. GRM is a feed-forward transformer-based model that efficiently incorporates multi-view information to translate the input pixels into pixel-aligned Gaussians, which are unprojected to create a set of densely distributed 3D Gaussians representing a scene. Together, our transformer architecture and the use of 3D Gaussians unlock a scalable and efficient reconstruction framework. Extensive experimental results demonstrate the superiority of our method over alternatives regarding both reconstruction quality and efficiency. We also showcase the potential of GRM in generative tasks, i.e., text-to-3D and image-to-3D, by integrating it with existing multi-view diffusion models. Our project website is at: this https URL .

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Stories of Graduate Student Success: Nidhi Nellore, doctoral student in the Department of Computer Science

• March 25, 2024
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Nidhi Nellore, a third-year doctoral student in the Computer Science program at Marquette University, was a finalist in the  Three Minute Thesis (3MT) Competition  that was held on Friday, Feb. 23, in the Weasler Auditorium. The 3MT is a global research competition that challenges participants to effectively explain their research in three minutes or less to a non-specialist audience. Participants can win prizes and represent Marquette University at the regional competition.

Nellore’s 3MT presentation was titled, “ Decoding Digital Health: Unraveling Sentiments and Topics in Femtech App Reviews in the Post-Dobbs Era .”   Nellore has been working with Dr. Michael Zimmer in the Social and Ethical Computing Lab. Her research investigates Femtech, which combines technology and women’s health, to understand how mobile apps address user needs and privacy concerns, especially following significant legal rulings like the Dobbs decision. Nellore’s research aims to improve women’s healthcare by providing insights into user experiences, privacy safeguards, and recommendations for app developers.

When asked why she chose to participate in the 3MT Competition, Nellore noted, “the experience offered a unique chance to present my work to a wider audience. It is also important to let the public know that we are working towards research that can help solve important issues.” Nellore ultimately hopes her research will help to enhance Femtech apps to be more user-friendly, feature-rich, and secure, ensuring that women can access effective healthcare while maintaining their privacy in the digital age.

In addition to being a finalist at 3MT, Nellore recently won 3rd place in the 2024 Graduate School Research Poster Competition and has presented multiple research projects at the Association for Computing Machinery (ACM), the world’s largest educational and scientific society which unites computing educators, researchers and professionals to share resources and address the challenges in the field of computing.

When Nellore is not engaged in research, she enjoys spending time cooking, dancing, or hanging out with her dog.

Want to read similar stories or have a success story to share? Visit  Stories of Graduate Student Success  to learn more.

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