Ethan Che @ Columbia

Ethan W. Che

PhD Student, Columbia Business School
Decision, Risk, and Operations

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 model-based machine learning for developing new computational approaches for large-scale problems in OR/MS. Some tools I use in my research are,

Differentiable Simulation
Optimal Control
Deep Reinforcement Learning
Stochastic Modeling (e.g. fluid and diffusion models)

In particular, I am interested in applications in adaptive experimentation and scheduling in queuing networks and discrete-event systems. I've also done research in mechanism design.

I worked as an ML engineer intern at Instacart in Spring 2023. I am a research intern at Netflix for Summer 2024.

eche25 (at) gsb.columbia.edu Google Scholar

Under Review

	Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches Ethan Che and Hongseok Namkoong NeurIPS 2022 Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems Major Revision at Operations Research. arXiv workshop
	Differentiable Discrete Event Simulation for Queuing Network Control Ethan Che, Jing Dong, and Hongseok Namkoong ICML 2023 Differentiable Almost Everything Workshop pdf workshop
	Stochastic Gradient Descent with Adaptive Data Ethan Che, Jing Dong, and Xin T. Tong pdf
	Planning Adaptive Experiments: A Math Programming Approach Ethan Che, Jimmy Wang, Daniel Jiang and Hongseok Namkoong NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World workshop