Doctoral Degrees Conferred

Academic Year: 2023-2024
Name	Thesis Advisor(s)	Thesis Title
Daniel Anderson	Guy E. Blelloch	Parallel Batch-Dynamic Algorithms Dynamic Trees, Graphs, and Self-Adjusting Computation
Isaac Grosof	Mor Harchol-Balter	Optimal Scheduling in Multiserver Queues
Pallavi Koppol	Reid Simmons, Henny Admoni	Interactive Machine Learning from Humans: Knowledge Sharing via Mutual Feedback
Katherine Kosaian	André Platzer	Formally Verifying Algorithms for Real Quantifier Elimination
Tian Li	Virginia Smith	Scalable and Trustworthy Learning in Heterogeneous Networks
Chun Kai Ling	J. Zico Kolter	Scalable Learning and Solving of Extensive-Form Games
Francisco Maturana	Rashmi Vinayak	Designing storage codes for heterogeneity: theory and practice
Kevin Pratt	Ryan O'Donnell	Hypergraph Rank and Expansion
Yuanhao Wei	Guy E. Blelloch	General Techniques for Efficient Concurrent Data Structures
Han Zhang	Yuvraj Agarwal, Matt Fredrikson	Secure and Practical Splitting of IoT Device Functionalities
Hanrui Zhang	Vincent Conitzer	Designing and Analyzing Machine Learning Algorithms in the Presence of Strategic Behavior

Academic Year: 2022-2023
Name	Thesis Advisor(s)	Thesis Title
Ainesh Bakshi	Pravesh K. Kothari, David P. Woodruff	Algorithms for Learning Latent Models: Establishing Tractability to Approaching Optimality
Benjamin Berg	Mor Harchol-Balter	A Principled Approach to Parallel Job Scheduling
Emily Black	Matt Fredrikson	(Un)Fairness Along the AI Pipeline: Problems and Solutions
Andrew Chung	Gregory R. Ganger	Realizing value in shared compute infrastructures
Chen Dan	Pradeep Ravikumar	Statistical Learning Under Adversarial Distribution Shift
Priya L. Donti	J. Zico Kolter, Inês Azevedo	Bridging Deep Learning and Electric Power Systems
Gabriele Farina	Tuomas Sandholm	Game-Theoretic Decision Making in Imperfect-Information Games: Learning Dynamics, Equilibrium Computation, and Complexity
Pratik Pramod Fegade	Todd C. Mowry, Phillip B. Gibbons, Tinaqi Chen	Auto-batching Techniques for Dynamic Deep Learning Computations
Shilpa Anna George	Mahadev Satyanarayanan (Satya)	Low-Bandwidth Remote Sensing of Rare Events
Graham Gobieski	Nathan Beckmann, Brandon Lucia	Programmable, Energy-minimal Computer Architectures
Paul Gölz	Ariel Procaccia	Social Choice for Social Good: Proposals for Democratic Innovation from Computer Science
D Ellis Hershkowitz	Bernhard Haeupler, R. Ravi	Compact Representations of Graphs and Their Metrics
Roger Iyengar	Mahadev Satyanarayanan (Satya)	Scaling Up Wearable Cognitive Assistance for Assembly Tasks
Ellango Jothimurugesan	Phillip B. Gibbons	Large-Scale Machine Learning over Streaming Data
Thomas Kim	David G. Andersen	Design principles for replicated storage systems built on emerging storage technologies
Soonho Kong	Edmund M. Clarke, Randal E. Bryant	An Efficient Delta-decision Procedure
Jack Kosaian	Rashmi Vinayak	Practical Coding-Theoretic Tools for Machine Learning Systems and by Machine Learning Systems
Michael Roman Kuchnik	George Amvrosiadis	Beyond Model Efficiency: Data Optimizations for Machine Learning Systems
Roie Levin	Anupam Gupta	Submodular Optimization Under Uncertainty
Guarav Manek	J. Zico Kolter	Stable Models and Temporal Difference Learning
Sai Sandeep Reddy Pallerla	Venkatesan Guruswami	New Directions in Inapproximability: Promise Constraint Satisfaction Problems and Beyond
Pedro Paredes	Ryan O'Donnell	On the Expansion of Graphs
Devdeep Ray	Srinivasan Seshan	Integrating Video Codec Design and Network Transport for Emerging Internet Video Streaming Application
Leslie Rice	J. Zico Kolter	Methods for robust training and evaluation of deep neural networks
Michael Rudow	Rashmi Vinayak	Efficient loss recovery for videoconferencing via streaming codes and machine learning
Ziv Scully	Mor Harchol-Balter, Guy E. Blelloch	A New Toolbox for Scheduling Theory
Yifan Song	Vipul Goyal	Communication Complexity of Information-Theoretic Multiparty Computation
Yong Kiam Tan	André Platzer	Deductive Verification for Ordinary Differential Equations: Safety, Liveness, and Stability
Alex L. Wang	Fatma Kilinc-Karzan	On Quadratically Constrained Quadratic Programs and their Semidefinite Program Relaxations
Ziqi Wang	Todd C. Mowry, Dimitrios Skarlatos	Building a More Efficient Cache Hierarchy by Taking Advantage of Related Instances of Objects
Kevin G. A. Waugh	J. Andrew Bagnell	Strategic Behavior Prediction
Sam Westrick	Umut Acar	Efficient and Scalable Parallel Functional Programming Through Disentanglement

Academic Year: 2021-2022
Name	Thesis Advisor(s)	Thesis Title
Sol Boucher	David G. Andersen	Lightweight Preemptible Functions
Daming Dominic Chen	Phillip B. Gibbons	Mitigating Memory-Safety Bugs with Efficient Out-of-Process Integrity Checking
Timothy Chu	Gary L. Miller	Machine Learning: Metrics and Embeddings
Ziqiang Feng	Mahadev Satyanarayanan	Human-efficient Discovery of Edge-based Training Data for Visual Machine Learning
Rajesh Jayaram	David Woodruff	Sketching and Sampling Algorithms for High-Dimensional Data
Anson Kahng	Ariel Procaccia	Computational Perspectives on Democracy
Ryan Kavanagh	Stephen Brookes, Frank Pfenning	Communication-Based Semantics for Recursive Session-Typed Processes
Klas Leino	Matt Fredrikson	Identifying, Analyzing, and Addressing Weaknesses in Deep Networks: Foundations for Conceptually Sound Neural Networks
Daehyeok Kim	Srinivasan Seshan, Vyas Sekar	Towards Elastic and Resilient In-Network Computing
Jason Li	Anupam Gupta, Bernhard Haeupler	Preconditioning and Locality in Algorithm Design
Lin Ma	Andrew Pavlo	Self-Driving Database Management Systems: Forecasting, Modeling, and Planning
Ravi Teja Mullapudi	Kayvon Fatahalian, Deva Ramanan	Dynamic Model Specialization for Efficient Inference, Training and Supervision
Vaishnavh Nagarajan	J. Zico Kolter	Explaining generalization in deep learning: progress and fundamental limits
Vidya Narayanan	James McCann	Foundations for 3D Machine Knitting
Namyong Park	Christos Faloutsos	Mining and Learning With Graphs and Tensors
Aurick Qiao	Eric P. Xing	Elastic Machine Learning Systems with Co-adaptation
Andrii Riazanov	Venkatesan Guruswami	Polar Codes with Near-Optimal Convergence to Channel Capacity
Nicholas Sharp	Keenan Crane	Intrinsic Triangulations in Geometry Processing
Jonathan Sterling	Robert W. Harper	First Steps in Synthetic Tait Computability: The Objective Metatheory of Cubical Type Theory
Petar Stojanov	Jaime G. Carbonell, Kun Zhang	Towards More Efficient and Data-Driven Domain Adaptation
Ellen Vitercik	Maria-Florina Balcan, Tuomas Sandholm	Automated algorithm and mechanism configuration
Di Wang	Jan Hoffmann	Static Analysis of Probabilistic Programs: An Algebraic Approach
Ruosong Wang	Ruslan Salakhutdinov	Tackling Challenges in Modern Reinforcement Learning: Long Planning Horizon and Large State Space
Samuel Yeom	Matt Fredrikson	Black-Box Approaches to Fair Machine Learning
Christopher Yu	Keenan Crane	Repulsive Energies and their Applications
Yu Zhao	Ryan O'Donnell	Generalizations and Applications of Hypercontractivity and Small-Set Expansion
Tiancheng Zhi	Srinivasa G. Narasimhan, Martial Hebert	Training Deep Networks with Material-Aware Supervision

Academic Year: 2020-2021
Name	Thesis Advisor(s)	Thesis Title
Abutalib Aghayev	Georget Amvrosiadis	Adopting Zoned Storage in Distributed Storage Systems
Naama Ben David	Guy Blelloch	Theoretical Foundations for Practical Concurrent and Distributed Computation
Rose Bohrer	Andre Platzer	Practical End-to-End Verification of Cyber-Physical Systems
Noam Brown	Tuomas Sandholm	Equilibrium Finding for Large Adversarial Imperfect-Information Games
Evan Cavallo	Robert Harper	Higher Inductive Types and Internal Parametricity for Cubical Type Theory