E-mail: bhzhang at cs dot cmu dot edu
Office: GHC 9225
CV, Google Scholar
I am a fourth-year PhD student in the Computer Science Department at Carnegie Mellon University, where I am fortunate to be advised by Tuomas Sandholm. Prior to CMU, I completed my undergraduate and master’s degrees at Stanford University, where I worked with Greg Valiant.
My current research interests lie in computational game theory, especially equilibrium-finding in large games. I have also done work in adversarial robustness, fairness in machine learning, and quantum computing.
Publications
“with” denotes alphabetical ordering of authors. * denotes equal contribution.
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Steering No-Regret Learners to Optimal Equilibria
Brian Hu Zhang*, Gabriele Farina*, Ioannis Anagnostides, Federico Cacciamani, Stephen McAleer, Andreas Haupt, Andrea Celli, Nicola Gatti, Vincent Conitzer, Tuomas Sandholm
arXiv 2023 -
Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games
Brian Hu Zhang*, Gabriele Farina*, Ioannis Anagnostides, Federico Cacciamani, Stephen McAleer, Andreas Haupt, Andrea Celli, Nicola Gatti, Vincent Conitzer, Tuomas Sandholm
arXiv 2023 -
Subgame Solving in Adversarial Team Games
Brian Hu Zhang*, Luca Carminati*, Federico Cacciamani, Gabriele Farina, Pierriccardo Olivieri, Nicola Gatti, Tuomas Sandholm
NeurIPS 2022 -
Polynomial-Time Optimal Equilibria with a Mediator in Extensive-Form Games
Brian Hu Zhang, Tuomas Sandholm
NeurIPS 2022 -
Optimal Correlated Equilibria in General-Sum Extensive-Form Games: Fixed-Parameter Algorithms, Hardness, and Two-Sided Column-Generation
Brian Hu Zhang, Gabriele Farina, Andrea Celli, Tuomas Sandholm
EC 2022 -
Team Belief DAG: A Concise Representation for Team-Correlated Game-Theoretic Decision Making
Brian Hu Zhang, Gabriele Farina, Tuomas Sandholm
ICML 2023; arXiv 2022 -
Polynomial-Time Sum-of-Squares Can Robustly Estimate Mean and Covariance of Gaussians Optimally
with Pravesh K. Kothari, Peter Manohar
ALT 2022 -
Team Correlated Equilibria in Zero-Sum Extensive-Form Games via Tree Decompositions
Brian Hu Zhang, Tuomas Sandholm
AAAI 2022 -
Subgame solving without common knowledge
Brian Hu Zhang, Tuomas Sandholm
NeurIPS 2021 Spotlight and AAAI Workshop on Reinforcement Learning in Games 2022 Oral Presentation -
Finding and Certifying (Near-)Optimal Strategies in Black-Box Extensive-Form Games
Brian Hu Zhang, Tuomas Sandholm
AAAI 2021 and AAAI Workshop on Reinforcement Learning in Games 2021 Oral Presentation -
Small Nash Equilibrium Certificates in Very Large Games
Brian Hu Zhang, Tuomas Sandholm
NeurIPS 2020 -
Sparsified Linear Programming for Zero-Sum Equilibrium Finding
Brian Hu Zhang, Tuomas Sandholm
ICML 2020 -
A Spectral View of Adversarially Robust Features
Shivam Garg, Vatsal Sharan*, Brian Hu Zhang*, Gregory Valiant
NeurIPS 2018 Spotlight -
Mitigating Unwanted Biases with Adversarial Learning
Brian Hu Zhang, Blake Lemoine, Margaret Mitchell
AIES 2018 -
Advantages of Unfair Quantum Ground-State Sampling
Brian Hu Zhang, Gene Wagenbreth, Victor Martin-Mayor, Itay Hen
Scientific Reports 2016
Teaching and Service
Service
- Peer Review: AAAI 2021 (selected as top 25% PC member), NeurIPS 2021, AAAI 2022, AAAI 2022 Workshop on Reinforcement Learning in Games, ICLR 2022, ICML 2022, NeurIPS 2022, ICLR 2023, AAAI 2023, ICML 2023, NeurIPS 2023
- CMU Theory Lunch Organizer, with Magdalen Dobson (Spring) & Praneeth Kacham (Fall), 2021
Teaching Assistanceships
Carnegie Mellon University
Stanford University
- CS 161 Design and Analysis of Algorithms, Winter 2019
- CS 227B General Game Playing, Spring 2017–2019
- CS 106A Programming Methodology, Spring–Summer 2016
- CS 106B Programming Abstractions, Fall 2016–Winter 2017