SCS Ph.D. Graduation 2019

Doctoral Degrees Conferred

Academic Year: 2022-2023
Name Thesis Advisor(s) Thesis Title
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
Michael J. Coblenz Jonathan Aldrich, Brad Myers User-Centered Design of Principled Programming Languages
Ankush Das Jan Hoffmann Resource-Aware Session Types for Digital Contracts
Henry DeYoung Frank Pfenning Session-Typed Ordered Logical Specifications
Laxman Dhulipala Guy Blelloch Provably Efficient and Scalable Shared- Memory Graph Algorithms
Saurabh Kadekodi Greg Ganger, Rashmi Vinayak DISK-ADAPTIVE REDUNDANCY: tailoring data redundancy to disk-reliability-heterogeneity in cluster storage systems
Deborah Stephanie Surden Katz Claire LeGoues Identification of Software Failures in Complex Systems Using Low-Level Execution Data
Jay Yoon Lee Jaime Carbonell, William Cohen Injecting output constraints into neural NLP models
Benjamin Lengerich Eric Xing Sample-Specific Models for Precision Medicine
Charles John McGuffey Phil Gibbons Modernizing Models and Management of the Memory Hierarchy for Non-Volatile Memory
Prashanth Menon Todd Mowry, Andy Pavlo On Building Robustness into Compilation-Based Main-Memory Database Query Engines
Georg P. Schoenherr Jaime Carbonell, Bhiksha Raj The Nonlinearity Coefficient - A Practical Guide to Neural Architecture Design
Amirbehshad Shahrasbi Bernhard Haeupler Coding for Synchronization Errors
Evan Shimizu Kayvon Fatahalian, James McCann Improving Parameterized Design with Interactive User-Guided Sampling and Parameter Identification Tools
Rui Silva Manuela Veloso, Francisco Melo Counterfactual MDPs: Planning Beyond Direct Control
Dana Van Aken Andy Pavlo On Automatic Database Management System Tuning Using Machine Learning
David Wajc Bernhard Haeupler Matching Theory under Uncertainty
Tianlong Yu Srinivasan Seshan, Vyas Sekar Securing Internet-of-Things via Fine-grained Network Detection and Prevention
Yimeng Zhang Tai Sing Lee Modeling early visual cortex using neural network models with recurrent circuits
Qing Zheng George Amvrosiadis, Garth Gibson Distributed Metadata and Streaming Data Indexing as Scalable Filesystem Services
Goran Žužić Bernhard Haeupler Towards Universal Optimality in Distributed Optimization
Academic Year: 2019-2020
Name Thesis Advisor(s) Thesis Title
Carlo Angiuli Robert Harper Computational Semantics of Cartesian Cubical Type Theory
Vijay Bhattiprolu Venkatesan Guruswami On the Approximability of Injective Tensor Norm
Logan Brooks Roni Rosenfeld Pancasting: forecasting epidemics from provisional data
Zack Coker Claire LeGoues Automatic repair of framework applications
Dhivya Eswaran Christos Faloutsos Mining Anomalies Using Static and Dynamic Graphs
Hannah Gommerstadt Frank Pfenning, Limin Jia Session-Typed Concurrent Contracts
Angela Jiang Greg Ganger Improving Deep Learning Training and Inference with Dynamic Hyperparameter Optimization