I am a PhD student at Columbia Business School in the Decision, Risk, and Operations division.
I am advised by Jing Dong
and Hongseok Namkoong.
My research is on structured reinforcement learning for large-scale decision-making problems in operations research.
In particular, I've developed new algorithmic learning approaches in the domains of (1) bandit exploration and adaptive learning
and (2) scheduling in discrete-event systems. My work uses tools from control theory, RL, gradient estimation, and stochastic modeling.
I worked as an ML engineer intern at Instacart in Spring 2023 working on causal machine learning. In Summer 2024, I was an ML Research intern at Netflix working on bandit exploration and uncertainty quantification.
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How Well do LLMs Compress Their Own Chain-of-Thought? |
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Differentiable Discrete Event Simulation for Queuing Network Control |
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Mathematical Programming for Adaptive Experiments |
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Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches |
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Stochastic Gradient Descent with Adaptive Data Ethan Che, Jing Dong, and Xin T. Tong |
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AExGym: Benchmarks and Environments for Adaptive Experimentation |
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Exploration Sizing via Model-Predictive Control |
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Discounting in Markov Chain Estimation |
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QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers |
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Robustly Optimal Auction Design under Mean Constraints |