E-mail: bhzhang at cs dot cmu dot edu
Office: GHC ~~9231~~ 9011
CV, Google Scholar

I am a PhD student in the Computer Science Department at Carnegie Mellon University, where I am fortunate to be advised by Prof. Tuomas Sandholm. I am supported by the CMU Hans J. Berliner Graduate Fellowship in Artificial Intelligence. I am on the academic job market this cycle!

My current research interests lie in computational game theory, especially equilibrium computation in extensive-form games; subgame solving; no-regret learning in games; automated mechanism design; adversarial team games; and solution concepts involving correlation, communication, and/or mediation. I have also done work in adversarial robustness, fairness in machine learning, and quantum computing.

Prior to CMU, I completed my undergraduate and master’s degrees at Stanford University, where I completed my honors thesis with Prof. Gregory Valiant.

Publications

(αβ) denotes alphabetical ordering of authors.
* denotes equal contribution.

Learning a Game by Paying the Agents
Brian Hu Zhang*, Tao Lin*, Yiling Chen, Tuomas Sandholm
arXiv 2025
Expected Variational Inequalities
Brian Hu Zhang*, Ioannis Anagnostides*, Emanuel Tewolde, Ratip Emin Berker, Gabriele Farina, Vincent Conitzer, Tuomas Sandholm
arXiv 2025
Learning and Computation of Φ-Equilibria at the Frontier of Tractability
Brian Hu Zhang*, Ioannis Anagnostides*, Emanuel Tewolde, Ratip Emin Berker, Gabriele Farina, Vincent Conitzer, Tuomas Sandholm
arXiv 2025
Computing Game Symmetries and Equilibria That Respect Them
Emanuel Tewolde, Brian Hu Zhang, Caspar Oesterheld, Tuomas Sandholm, Vincent Conitzer
AAAI 2025 Oral Presentation, Best Poster Award
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
Mathematics of Operations Research 2025
Extended version of [11] (EC 2022)
A Lower Bound on Swap Regret in Extensive-Form Games
(αβ) Constantinos Daskalakis, Gabriele Farina, Noah Golowich, Tuomas Sandholm, Brian Hu Zhang
arXiv 2024
Efficient Φ-Regret Minimization with Low-Degree Swap Deviations in Extensive-Form Games
Brian Hu Zhang, Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm
NeurIPS 2024
Exponential Lower Bounds on the Double Oracle Algorithm in Zero-Sum Games
Brian Hu Zhang, Tuomas Sandholm
IJCAI 2024
Imperfect-Recall Games: Equilibrium Concepts and Their Complexity
Emanuel Tewolde, Brian Hu Zhang, Caspar Oesterheld, Manolis Zampetakis, Tuomas Sandholm, Paul W. Goldberg, Vincent Conitzer
IJCAI 2024
Hidden-Role Games: Equilibrium Concepts and Computation
Luca Carminati*, Brian Hu Zhang*, Gabriele Farina, Nicola Gatti, Tuomas Sandholm
EC 2024
Steering No-Regret Learners to a Desired Equilibrium
Brian Hu Zhang*, Gabriele Farina*, Ioannis Anagnostides, Federico Cacciamani, Stephen McAleer, Andreas Haupt, Andrea Celli, Nicola Gatti, Vincent Conitzer, Tuomas Sandholm
EC 2024
Mediator Interpretation and Faster Learning Algorithms for Linear Correlated Equilibria in General Extensive-Form Games
Brian Hu Zhang, Gabriele Farina, Tuomas Sandholm
ICLR 2024
On the Outcome Equivalence of Extensive-Form and Behavioral Correlated Equilibria
Brian Hu Zhang, Tuomas Sandholm
AAAI 2024
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
NeurIPS 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
Extended by [23] (Mathematics of Operation Research 2025)
Team Belief DAG: Generalizing the Sequence Form to Team Games for Fast Computation of Correlated Team Max-Min Equilibria via Regret Minimization
Brian Hu Zhang, Gabriele Farina, Tuomas Sandholm
ICML 2023
Earlier version presented at ICLR Workshop on Gamification and Multiagent Solutions (GMAS) 2022
Polynomial-Time Sum-of-Squares Can Robustly Estimate Mean and Covariance of Gaussians Optimally
(αβ) Pravesh K. Kothari, Peter Manohar, Brian Hu Zhang
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
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
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
Nature Scientific Reports 2016

Teaching and Service

Service

Peer Review:
- AAAI: 2021–present
- NeurIPS: 2021–present
- ICLR: 2022–present
- ICML: 2022–present
- Journals: JAIR, IEEE TAI, Knowledge-Based Systems, Intl J Approximate Reasoning
- Workshops: AAAI Reinforcement Learning in Games
CMU Theory Lunch Organizer, with Magdalen Dobson (Spring) & Praneeth Kacham (Fall), 2021