Doctoral Degrees Conferred Search Academic Year: 2024-2025 Name Thesis Advisor(s) Thesis Title Jatin Arora Umut A. Acar Provably Efficient Coscheduling of Computation and Memory through Disentanglement Mihir Bala Mahadev Satyanarayanan Towards Fully-Autonomous Ultralight Drones Lucio Mwinmaarong Dery Graham Neubig, Ameet Talwalkar On Resource Efficient Transfer Learning via End Task Aware Training Magdalen Dobson Manohar Guy E. Blelloch New Techniques for Parallelism and Concurrency in Nearest Neighbor Search Travis Hance Bryan Parno Verifying Concurrent Systems Code Ananya Joshi Roni Rosenfeld, Bryan Wilder Event Monitoring in Modern Public Health Data Streams Praneeth Kacham David P. Woodruff On Efficient Sketching Algorithms David M. Kahn Jan Hoffmann Liveraging Linearity to Improve Automatic Amortized Resource Analysis Shiva Kaul Geoffrey Gordon Classical Improvements to Modern Machine Learning Mikhail Khodak Maria-Florina Balcan, Ameet Talwalkar The Learning of Algorithms and Architectures Jenny Lin James McCann Formalizing Object Equivalence in Machine Knitting Peter Manohar Venkatesan Guruswami, Pravesh K. Kothari New Spectral Techniques in Algorithms, Combinatorics, and Coding Theory: The Kikuchi Matrix Method Elisaweta Masserova Bryan Parno, Vipul Goyal Distributed Cryptography as a Service Yue Niu Robert Harper Cost-sensitive Programming, Verification, and Semantics Justin Raizes Vipual Goyal Quantum Approaches to Verifiable Deletion Dravyansh Sharma Maria-Florina Balcan Data-Driven Algorithm Design and Principled Hyperparameter Tuning in Machine Learning Eric Mark Sturzinger Mahadev Satyanarayanan Survival-Critical Machine Learning Asher James Trockman J. Zico Kolter Mimetic Initialization for Deep Neural Networks Haithem Turki Deva Ramanan Towards City-Scale Neural Rendering Ranysha Ware Justine Sherry, Srinivasan Seshan Battle for Bandwidth: On the Deployability of New Congestion Control Algorithms Justin Alexander Whitehouse Zhiwei Steven Wu, Aaditya Ramdas Modern Martingale Methods: Theory and Applications Daniel Lin-Kit Wong Gregory R. Ganger Machine Learning for Flash Caching in Bulk Storage Systems Juncheng Yang Rashmi Vinayak Designing Efficient and Scalable Key-value Cache Management Systems Minji Yoon Christos Faloutsos, Ruslan Salakhutdinov Deep Learning on Graphs: Tackling Scalability, Privacy, and Multimodality Runtian Zhai Pradeep Ravikumar, Zico Kolter Contextures: The Mechanism of Representation Learning Yi Zhou Bryan Parno Towards Scalable Automated Program Verification for System Software Anders Øland Roger Dannenberg, Bhiksha Raj Efficient Deep Learning 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 Jay Bosamiya Bryan Parno A Principled Approach towards Unapologetic Security Matthew Butrovich Andrew Pavlo On Embedding Database Management System Logic in Operating Systems via Restricted Programming Environments Bailey Flanigan Ariel Procaccia Expanding our Participatory Democracy Toolkit using Algorithms, Social Choice, and Social Science Mark Gillespie Keenan Crane Evolving Intrinsic Triangulations Isaac Grosof Mor Harchol-Balter Optimal Scheduling in Multiserver Queues Yue (Sophie) Guo Katia Sycara Enhancing Policy Transfer in Action Advising for Reinforcement Learning Quang Minh Hoang Carl Kingsford Practical Methods for Automated Algorithm Design in Machine Learning and Computational Biology Steven Jecmen Nihar B. Shah, Fei Fang Making Peer Review Robust to Undesirable Behavior Byungsoo Jeon Tianqi Chen, Zhihao Jia Automated and Portable Machine Learning Systems 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 Abhiram Kothapalli Bryan Parno A Theory of Composition for Proofs of Knowledge 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 Klaas Pruiksma Frank Pfenning Adjoint Logic with Applications Jielin Qiu Christos Faloutsos, Lei Li On the Alignment, Robustness, and Generalizability of Multimodal Learning Rohan Sawhney Keenan Crane Monte Carlo Geometry Processing: A Grid-Free Approach to Solving Partial Differential Equations on Volumetric Domains Siva Kamesh Somayyajula Frank Pfenning Total Correctness Type Refinements for Communicating Processes Yuanhao Wei Guy E. Blelloch General Techniques for Efficient Concurrent Data Structures Jalani K. Williams Weina Wang Setup Times in Multiserver Systems Ke Wu Elaine Shi What Can Cryptography Do For Transaction Fee Mechanism Design Taisuke Yasuda David P. Woodruff Algorithms for Matrix Approximation: Sketching, Sampling, and Sparse Optimization 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 Giulio Zhou David G. Andersen Building reliable and transparent machine learning systems using structured intermediate representations 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, Tianqi 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. 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