Assistant Professor
Operations Management
Booth School of Business
University of Chicago
Affiliations at Booth:
Center for Applied AI
Tolan Center for Healthcare
Education
- BSE in Operations Research and Financial Engineering, Princeton
- PhD in Operations Research and Information Engineering, Cornell
Supervisor: Adrian S. Lewis - Post-doc & MS in Statistics, Stanford
Supervisor: David L. Donoho
NAME: Preferred name is X.Y.
CITIZENSHIP: United States 🇺🇸
EMAIL: XY (dot) Han (at) chicagobooth (dot) edu
Awards
🏆 ICLR 2022 Outstanding Paper Award
🏆 ICCOPT 2022 Best Paper Prize for Young Researchers (Finalist)
Most Recognized Work:
Discovered the neural collapse phenomenon in AI training [PNAS 2020; ICLR 2022].
Research Interest
Deconstructing the conventional wisdoms of AI.
Modern AI is built on a foundation of conventional wisdoms — architectures, optimizers, MLOps flows etc — discovered through countless feats of trial-and-error and engineering. Collectively, they enable AI to now perform near human-level decision-making, but we can’t precisely pinpoint why. My research deconstructs these successes to uncover the operational “first principles” of AI behavior.
Artificial Intelligence: Uncovering the “first principles” of AI by running large-scale experiments on used-in-practice AI architectures. Formulating mathematically-grounded models of their behavior from those experiments. (Prev. Work: Neural Collapse, MoE Load-Balancing)
Optimization: Mathematically distilling the fundamental structures present in nonsmooth, nonconvex, and nonlinear optimization problems. Using this knowledge to build faster and simpler optimization algorithms from the ground up. (Prev. Work: Survey Descent)
Applications: Translating this knowledge into productivity-enhancing tools. Focusing on solutions that amplify professional insights. Solving critical operational problems for collaborators in industry and civic institutions. (Past/Present Collabs: Frick Art Reference Library, Veolia North America LLC, Surgical Data Science Collective)
Publication Highlights
Most Influential Papers
Prevalence of Neural Collapse During the Terminal Phase of Deep Learning Training
Vardan Papyan*, X.Y. Han*, and David L. Donoho
(*Equal Contribution. Non-alphabetical to balance visibility in citations.)
Proceedings of the National Academy of Sciences (PNAS), 117.40 (2020): 24652-24663.
🌟 Discovered neural collapse, now a widely studied phenomenon in AI training.
Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path
X.Y. Han*, Vardan Papyan*, and David L. Donoho
International Conference on Learning Representations (ICLR) 2022, 26 April 2022. (Oral)
🏆ICLR 2022 Outstanding Paper Award
Most Recent Papers
A Theoretical Framework for Auxiliary-Loss-Free Load Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models
X.Y. Han* and Yuan Zhong*
NeurIPS 2025, MLxOR Workshop (arXiv:2512.03915)
💬 What can operations contribute to AI? This paper is my first answer. s-MoE load-balancing procedures are examples of ML heuristics that work very well in practice but we don’t have much rigorous understanding for why they would. In this paper, Yuan and I use mathematical tools from OR/OM to build a theoretical framework for analyzing why DeepSeek’s ALF-LB procedure is effective at s-MoE load-balancing.
Commit Activity (Live Updated):
X.Y. Han 