Jongmin Mun

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I am a third-year PhD student in the Data Sciences and Operations Department at the University of Southern California. I am advised by Prof. Yingying Fan and Prof. Paromita Dubey. My current research focuses on using reinforcement learning and causal inference to address problems in statistics, operations research and computational neuroscience, including high-dimensional clustering, dynamic pricing and site harmonization.

Before starting my PhD, I studied:

  1. High-dimensional neural signal analysis for the development of liquid-metal neural probe, with Dr. Young-Geun Park and Prof. Jang-Ung Park,
  2. Regression for mixed functional-Euclidean data for medical image analysis, with Prof. Jeong Hoon Jang,
  3. The use of generative models to address class imbalance in machine learning, with Prof. Jaeoh Kim, at the Center for Army Analysis and Simulations (CAAS), a research department of the Republic of Korea Army.
  4. Privacy–utility trade-off in private federated anayltics (A/B testing) using minimax statistical theory with Prof. Ilmun Kim,

News

Jan 25, 2026 A new paper for simultaneous supervised dimension reduction and regression in multimodal data is now on arxiv. Motivated by renal imaging studies where predictors consists of photon count curve with pharmacokinetic and demographic variables, the approach is designed to effectively handle intermodal correlations and improve prediction accuracy.
Dec 6, 2025 I presented my dynamic pricing paper at NeurIPS MLxOR workshop. Under the covariate shift and local differential privacy, we use pessimistic value-based off-policy learning algorithm for continuous treatment (pricing) assignment policy.
Nov 10, 2025 My paper has been published in the Journal of Machine Learning Research! 🎉 We propose private permutation-based two-sample tests incorporating U-statistic and Google’s RAPPOR mechanism, and we derive the minimax rates that characterize the privacy–utility trade-off.
May 26, 2025 My first paper as a PhD student is now on arXiv! We propose an efficient iterative high-dimensional clustering algorithm that bypasses model parameter estimation.
Nov 24, 2024 My collaboration on detecting sex differences in autism using brain connectome data is accepted by NeuroImage! I contributed by using a generative model to enhance testing power.