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Yuhan YeI am a first year PhD student at the MIT Center for Computational Science and Engineering (CCSE). I received my B.S. degree from the School of Mathematical Sciences, Peking University in 2025, with a major in applied and computational mathematics. I am fortunate to work with Professors Asu Özdağlar and Saurabh Amin. News: In summer 2026, I will attend COLT 2026 and the Princeton Machine Learning Theory Summer School. Looking forward to connecting there! |
Research
My research focuses on the design and theoretical analysis of algorithms for optimization and learning.
A central question in my work is what information, and how much of it, is truly needed to make optimal decisions. To address this, I develop learning algorithms that preserve decision quality while minimizing both the amount of information required and the size of the resulting optimization models. This work lies along the lines of beyond-worst-case analysis and data-driven algorithm design, connecting ideas from operations research, optimization, learning theory, and computational complexity.
Publications
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The Geometry of Linear Program Compression: An Exact Characterization and Learning Algorithm
arXiv preprint, 2026. [arxiv]
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Parameterized Complexity of Stationarity Testing for Piecewise-Affine Functions and Shallow CNN Losses
arXiv preprint, 2026. [arxiv]
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Learning Decision-Sufficient Representations for Linear Optimization
, Saurabh Amin, Asuman Özdağlar
COLT 2026. [arxiv] [conference] [slides]
STOC 2026 workshop: Machine Learning for Algorithms. [workshop] [slides]
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An Online Adaptive Sampling Algorithm for Stochastic Difference-of-convex Optimization with Time-varying Distributions
ICML 2025, oral. [pdf] [conference] [poster]
1. Efficient algorithms and lower bounds for nonsmooth nonconvex optimization
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An Online Adaptive Sampling Algorithm for Stochastic Difference-of-convex Optimization with Time-varying Distributions
ICML 2025, oral. [pdf] [conference] [poster]
-
Parameterized Complexity of Stationarity Testing for Piecewise-Affine Functions and Shallow CNN Losses
arXiv preprint, 2026. [arxiv]
2. Learning compressed representations for optimization models
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Learning Decision-Sufficient Representations for Linear Optimization
, Saurabh Amin, Asuman Özdağlar
COLT 2026. [arxiv] [conference] [slides]
STOC 2026 workshop: Machine Learning for Algorithms. [workshop] [slides]
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The Geometry of Linear Program Compression: An Exact Characterization and Learning Algorithm
arXiv preprint, 2026. [arxiv]
Contact
Feel free to reach out to me at:
- Email: yyh03@mit.edu
Miscellaneous
I like playing chess, while I am struggling to improve my ELO on chess.com. I also play other strategy games.