School of Computer Science Distinguished Dissertation Award
– Awarded annually by the School of Computer Science in recognition of outstanding work by a graduate of our school-wide doctoral programs.

2023-2024 (Award 2024)

• Paul Pu Liang, May 2024 (MLD)
  Foundations of Multisensory Artificial Intelligence
  –Advisors: Louis-Philippe Morency, Ruslan Salakhutdinov
  
  

• Travis Hance, August 2024 (CSD)
  Verifying Concurrent Systems Code
  –Advisor: Bryan Parno
  
• Mikhail Khodak, August 2024 (CSD)
  The Learning of Algorithms and Architectures
  –Advisors: Maria-Florina Balcan, Ameet Talwalkar
  
• Rohan Sawhney, May 2024 (CSD)
  Monte Carlo Geometry Processing: A Grid-Free Approach to Solving Partial Differential Equations on Volumetric Domains
  –Advisor: Keenan M. Crane
  

2022-2023 (Award 2023)

• Gabriele Farina, May 2023 (CS)
  Game-Theoretic Decision Making in Imperfect-Information Games - Learning Dynamics, Equilibrium Computation, and Complexity
  –Advisor: Tuomas Sandholm
  
  

• Amanda Coston, May 2023 (Heinz/MLD)
  Principled Machine Learning for Societally Consequential Decision Making
  –Advisors: Alexandra Chouldechova, Edward H. Kennedy
  
• Isaac Grosof, July 2023 (CSD)
  Optimal Scheduling in Multiserver Queues
  –Advisor: Mor Harchol-Balter
  
• Dongshunyi (Dora) Li, July 2023 (CBD)
  Spatio-Temporal Analysis of Single Cell Data
  –Advisor: Ziv Bar-Joseph
  
• Hanrui Zhang, July 2023 (CSD)
  Designing and Analyzing Machine Learning Algorithms in the Presence of Strategic Behavior
  –Advisor: Vincent Conitzer

• Yong Kiam Tan, June 2022 (CS)
  Deductive Verification for Ordinary Differential Equations: Safety, Liveness, and Stability
  –Advisor: André Platzer
  
  

• Emily Black, July 2021 (CSD)
  (Un)Fairness Along the AI Pipeline Problems and Solutions
  –Advisor: Matt Fredrikson
  
• Ziv Scully, August 2022 (CSD)
  A New Toolbox for Scheduling Theory
  –Advisor: Mor Harchol-Balter
  
• Jonathan Sterling, October 2021 (CSD)
  First Steps in Synthetic Tait Computability: The Objective Metatheory of Cubical Type Theory
  –Advisor: Robert Harper

• Ellen Vitercik, August 2021 (CS)
  Automated algorithm and mechanism configuration
  –Advisors: Marina-Florina Balcan, Tuomas Sandholm
  
  

• Rose Bohrer, May 2021 (CSD)
  Practical End-to-End Verification of Cyber-Physical Systems
  –Advisor: André Platzer
  
• Jason Li, June 2021 (CSD)
  Preconditioning and Locality in Algorithm Design
  –Advisors: Anupam Gupta, Bernhard Haeupler
  
• Toby Jia-Jun Li, May 2021 (HCII)
  A Multi-Modal Intelligent Agent that Learns from Demonstrations and Natural Language Instructions
  –Advisor: Brad A. Myers
  
• Yang Yang, December 2020 (CBD)
  Computational methods for multi-species comparison of 3D genome organization and function
  –Advisor: Jian Ma

2019/2020 (Award 2020)

• Noam Brown, September 2020 (CSD)
  Equilibrium Finding for Large Adversarial Imperfect-Information Games
  –Advisor: Tuomas Sandholm
  
  

  Honorable Mention

• Naama Ben-David, August 2020 (CSD)
  Theoretical Foundations for Practical Concurrent and Distributed Computation
  –Advisor: Guy E. Blelloch
  Laxman Dhulipala, August 2020 (CSD)
  Provably Efficient and Scalable Shared-Memory Graph Processing
  –Advisor: Guy E. Blelloch
  
• Chieh Lin, March 2020 (MLD)
  Probabilistic Single Cell Lineage Tracing
  –Advisor: Ziv Bar-Joseph
  
• Eric Wong, May 2020 (MLD)
  Provable, structured, and efficient methods for robustness of deep networks to adversarial examples
  –Advisor: J. Zico Kolter

  
  

• Carlo Angiuli, September 2019 (CS)
  Computational Semantics of Cartesian Cubical Type Theory
  –Advisor: Robert Harper
  
  

  Honorable Mention

• Simon Shaolei Du, April 2019 (MLD)
  Gradient Descent for Non-convex Problems in Modern Machine Learning
  –Advisors: Barnabás Póczos, Aarti Singh
  
• Kenneth Holstein, September 2019 (HCII)
  Designing Real-time Teacher Augmentation to Combine Strengths of Human and AI Instruction
  –Advisors: Vincent Aleven, Bruce M McLaren
  
• Lerrel Joseph Pinto, August 2019 (RI)
  Data Centric Robotic Learning
  –Advisor: Abhinav Gupta
  
• Sabrina Rashid, 2019 (CBD)
  Distributed Computing in Nature
  –Advisor: Ziv Bar-Joseph

  
  

• Nika Haghtalab , August 2018 (CS)
  Foundation of Machine Learning, by the People, for the People
  –Advisors: Avrim Blum, Ariel Procaccia
  

  
  

• Kuen-Bang Hou (Favonia) , February 2017 (CS)
  Higher-Dimensional Types in the Mechanization of Homotopy Theory
  –Advisor: Robert Harper
  

• Junchen Jiang , September 2017 (CS)
  Enabling Data-Driven Optimization of Quality of Experience in Internet Applications
  –Advisors: Vyas Sekar (ECE), Hui Zhang (CSD)

  
  

• Kristina Sojakova , August 2016 (CS)
  Higher Inductive Types as Homotopy-Initial Algebras
  –Advisors: Steven Awodey, Frank Pfenning
  

  
  Honorable Mention

• John Wright, May 2016 (CS)
  How to Learn a Quantum State
  –Advisor: Ryan O'Donnell

• Sarah Loos, February 2016 (CS)
  Differential Refinement Logic
  –Advisor: André Platzer

  
  

• Julian Shun , May 2015 (CS)
  Shared-Memory Parallelism Can Be Simple, Fast, and Scalable
  –Advisor: Guy Blelloch
  

  
  Honorable Mention

• David Bamman, 2015 (LTI)
  People-Centric natural Language Processing
  –Advisor: Noah Smith

• Anca Dragan, 2015 (RI)
  Legible Robot Motion Planning
  –Advisor: Siddhartha Srinivasa

• Danai Koutra, 2015 (CS)
  Exploring and Making Sense of Large Graphs
  –Advisor: Christos Faloutsos

  
  

• Siddharth Gopal, August 2014 (LTI)
  Large-scale Structured Learning
  –Advisor: Yiming Yang
  

  
  

• Joseph Gonzalez , December 2012 (ML)
  Parallel and Distributed Systems for Probabilistic Reasoning
  –Advisor: Carlos Guestrin
  
• Richard Peng , August 2013 (CS)
  Design Using Spectral Graph Theory
  –Advisor: Gary Miller

  Honorable Mention

• Sicun (Sean) Gao, 2012 (CS)
  Computable Analysis, Decision Procedures, and Hybrid Automata: A New Framework for the Formal Verification of Cyber-Physical Systems
  –Advisors: Edmund Clarke, Jeremy Avigad

  
  

• Kanat Tanwongsan, August 2011 (CS)
  Efficient Parallel Approximation Algorithms
  –Advisors: Guy Blelloch, Anupam Gupta
  

• Vijay Vasudevan, October 2011 (CS)
  Energy-efficient Data-intensive Computing with a Fast Array of Wimpy Nodes
  –Advisor: David Andersen
  

  Honorable Mention

• André Filipe Torres Martins, May 2011 (IST/Portugal)
  The Geometry of Constrained Structured Prediction: Applications to
  Inference and Learning of Natural Language Syntax
  –Advisors: Noah Smith, Eric Xing, Mário Figueiredo, Pedro Aguiar

• Duen Horng Polo Chau, July 2012 (ML)
  Data Mining Meets HCI: Making Sense of Large Graphs
  –Advisor: Christos Faloutsos

2010/2011 (Award 2011)

• Jean-Francois Lalonde, December 2011 (RI)
  Understanding and Recreating Visual Appearance Under Natural Illumination
  –Advisors: Alexei Efros, Srinivasa Narasimhan
  

  Honorable Mention

• Daniel Licata, February 2011 (CS)
  Dependently Typed Programming with Domain-Specific Logics
  –Advisor: Robert Harper

• Hetunandan Kamisetty, March 2011 (CS)
  Structured Probabilistic Models of Proteins Across Spatial and Fitness Landscape
  –Advisors: Christopher J. Langmead, Eric P. Xing

• Brian Ziebart, December 2010 (ML)
  Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy
  –Advisors: J. Andrew Bagnell, Anind K. Dey

2009/2010 (Award 2010)

• Yi Wu, August 2010 (CS)
  The Approximability of Learning and Constraint Satisfaction Programs
  –Advisor: Ryan O'Donnell
  

  Honorable Mention

• Deepak Garg, December 2009 (CS)
  Proof Theory for Authorization Logic and Its Applications to a Practical File System
  –Advisor: Frank Pfenning

2008/2009 (Award 2019)

• Maria-Florina Balcan, September 2008 (CS)
  New Theoretical Frameworks for Machine Learning
  –Advisor: Avrim Blum
  

  Honorable Mention

• Andreas Krause, December 2008 (CS)
  Optimizing Sensing: Theory and Applications
  –Advisor: Carlos Guestrin
• Jurij Leskovec, September 2008 (ML)
  Dynamics of Large Networks
  –Advisor: Christos Faloutsos

2007/2008

• Derek Hoiem, May 2007 (RI)
  Seeing the World Behind the Image: Spatial Layout for 3D Scene Understanding
  –Advisors: Alexei (Alyosha) Efros and Martial Hebert
  

  Honorable Mention

• Pradeep K. Ravikumar, August 2007 (ML)
  Approximate Inference Structure Learning and Feature Estimation in Markov Random Fields
  –Advisor: John Lafferty
• Ryan Williams, August 2007 (CS)
  Algorithms and Resource Requirements for Fundamental Problems
  –Advisor: Manuel Blum