SCS Ph.D. Graduation 2019

Doctoral Degrees Conferred

Academic Year: 2025-2026
Name Thesis Advisor(s) Thesis Title
Adithya Abraham Philip Justine Sherry Accurately Parameterizing Internet Performance Testing for Realistic Evaluations
Nirav Atre Justine Sherry Taming Model Incongruities in Networked Systems
Dorian Chan Matthew O'Toole Holographic Illumination for Computer Vision
Jun-Ting Hsieh Pravesh K. Kothari Algorithms and Explicit Constructions via Spectral Techniques
Suhas Jayaram Subramanya Greg Ganger Efficient and Responsive Job-Resource Co-adaptivity for Deep Learning Workloads in Large Heterogeneous GPU Clusters
Meng-Chieh Lee Christos Faloutsos, Leman Akoglu Explainable Mining of Graphs and Time Series: Algorithms and Applications
Sara McAllister Nathan Beckmann, Gregory R. Ganger Toward Sustainable Datacenters through Efficient Data Retrieval
Hojin Park George Amvrosiadis, Gregory R. Ganger Cost-Efficient Storage and Caching in Public Clouds
Long Pham Jan Hoffmann Hybrid Resource-Bound Analyses of Programs
Madhusudhan Reddy Pittu David Woodruff, Anupam Gupta Fairness, Diversity, Explainability, and Robustness for Algorithmic Decision-Making
Siddharth Prasad Maria-Florina Balcan, Tuomas Sandholm Mechanism Design and Integer Programming in the Data Age
Joseph E. Reeves Marijn J.H. Heule, Randal E. Bryant Cardinality Constraints in Boolean Satisfiability Solving
Benjamin Stoler Jean Oh Towards Robust Autonomous Driving and Social Robot Navigation via Enhanced Data Utilization
Arjun Teh Ioannis Gkioulekas, Matthew O'Tooler Computational Lens Design
Joshua Nathaniel Williams J. Zico Kolter Understanding Representations of Humans in Generative Image Modeling Through Discrete Counterfactual Prompt Optimization
Jeff (Sichao) Xu Pravesh K. Kothari Spectral Techniques for Average-Case Complexity
Brian Hu Zhang Tuomas Sandholm New Solution Concepts and Algorithms for Equilibrium Computation and Learning in Extensive-Form Games and Beyond
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 Leveraging 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
Mingjie Sun Elaine Shi, Guilia Fanti Hidden Properties of Large Language Models
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
Mingxun Zhou Elaine Shi, Guilia Fanti Private Information Retrieval and Searching with Sublinear Costs
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