SCS Ph.D. Graduation 2019

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
Matthew Butrovich Andrew Pavlo On Embedding Database Management System Logic in Operating Systems via Restricted Programming Environments
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