YoonHaeng Hur

Bio

I am a PhD candidate in the Department of Statistics at the University of Chicago. Before graduate school, I received a BS in Mathematical Sciences and a BA in Economics from Seoul National University.

Research Interests

My research tackles computational and statistical questions in modern data science using optimal transport as a primary guiding principle. Currently, I am interested in the following topics.

Theoretical foundations of unsupervised learning
Geometric data analysis based on invariant features
Applied causal inference

Contact: yoonhaenghur@uchicago.edu

Working Papers

A Convexified Matching Approach to Imputation and Individualized Inference
with T. Liang
[arXiv] [Code]
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction
with K. Dharmakeerthi, T. Liang
[arXiv]
Robust Point Matching with Distance Profiles
with Y. Khoo
[arXiv]

Publications

Detecting Weak Distribution Shifts via Displacement Interpolation
with T. Liang
Journal of Business & Economic Statistics (2025)
Reversible Gromov-Monge Sampler for Simulation-Based Inference
with W. Guo, T. Liang
SIAM Journal on Mathematics of Data Science (2024)
Generative Modeling via Tree Tensor Network States
with X. Tang, Y. Khoo, L. Ying
Research in the Mathematical Sciences (2023)
Generative Modeling via Tensor Train Sketching
with J. G. Hoskins, M. Lindsey, E.M. Stoudenmire, Y. Khoo
Applied and Computational Harmonic Analysis (2023) [Code]
Online Learning to Transport via the Minimal Selection Principle
with W. Guo, T. Liang, C.T. Ryan
Conference on Learning Theory (2022)

Bio

Research Interests

Working Papers

Publications

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