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I am broadly interested in algorithms for robust sequential decision-making, drawing from reinforcement learning, search, and game theory. My long-term goal is to create an agent that can do anything that a human can do on a computer. Toward this goal, I am currently working on foundation models for decision-making and AI alignment.
I am a postdoc at CMU working with Tuomas Sandholm. I received a PhD in computer science from the University of California, Irvine working with Pierre Baldi. During my PhD, I did research scientist internships at Intel Labs and DeepMind. Before that, I received my bachelor's degree in mathematics and economics from Arizona State University in 2017. I am currently on the job market! Please reach out if you think I will be a good fit. | |
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Research Language Models can Solve Computer Tasks Team-PSRO for Learning Approximate TMECor in Large Team Games via Cooperative Reinforcement Learning ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret Mastering the Game of Stratego With Model-Free Multiagent Reinforcement Learning XDO: A Double Oracle Algorithm for Extensive-Form Games Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games Solving the Rubik's Cube With Approximate Policy Iteration Llemma: An Open Language Model For Mathematics Confronting Reward Model Overoptimization with Constrained RLHF Language Models can Solve Computer Tasks Team-PSRO for Learning Approximate TMECor in Large Team Games via Cooperative Reinforcement Learning Policy Space Diversity for Non-Transitive Games Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games Algorithms and Complexity for Computing Nash Equilibria in Adversarial Team Games Regret-Minimizing Double Oracle for Extensive-Form Games MANSA: Learning Fast and Slow in Multi-Agent Systems A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret Mastering the Game of Stratego With Model-Free Multiagent Reinforcement Learning Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks Proving Theorems using Incremental Learning and Hindsight Experience Replay Independent Natural Policy Gradient Always Converges in Markov Potential Games XDO: A Double Oracle Algorithm for Extensive-Form Games Discovering Multi-Agent Auto-Curricula in Two-Player Zero-Sum Games Online Double Oracle Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination Solving the Rubik's Cube with Deep Reinforcement Learning and Search Solving the Rubik's Cube With Approximate Policy Iteration | |
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Teaching Tuomas and I are co-teaching a course on computational game solving this semester. The first half focuses on fundamental concepts in game theory and the second half covers state-of-the art methods on large games such as Stratego and Diplomacy. We emphasize the intersection of concepts from reinforcement learning and game theory in state-of-the-art methods. | |
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