Jongmin Mun

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I am a second-year PhD student in the Data Sciences and Operations Department at the University of Southern California. My current research focuses on high-dimensional clustering using semi-definite programming, advised by Prof. Yingying Fan and Prof. Paromita Dubey. I investigated the privacy-utility trade-off in private two-sample (A/B) testing using minimax statistical theory under the guidance of Prof. Ilmun Kim, during my Master’s in Statistics from Yonsei University in Seoul, South Korea. Prior to that, I served as an artificial intelligence researcher at the Center for Army Analysis and Simulations (CAAS) for the Republic of Korea Army, focusing on class imbalance issues in statistical learning, advised by Prof. Jaeoh Kim. I also completed my Bachelor’s degree in Statistics at Yonsei University.

News

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.
Nov 13, 2024	My work with my master’s advisor, Ilmun Kim, is now on arXiv! I devised minimax-optimal private tests for discrete and continuous data and established the fundamental limits of private two-sample testing.
Nov 1, 2024	A paper accepted by Computational Statistics & Data Analysis! I developed a theory on using a generative model to enhance classifier performance.
Feb 13, 2024	My collaboration on predicting wildfires in military artillery training is accepted by the Journal of Classification! I contributed by leveraging a generative model to improve classification performance. .
Feb 1, 2024	My collaboration on the Nobel Soft Neural Probe is accepted by Nature Communications! I contributed by clustering and testing high-dimensional neural signals to analyze the probe’s performance.

Selected Publications

Privacy

Minimax optimal two-sample testing under local differential privacy

Jongmin Mun, Seungwoo Kwak, and Ilmun Kim

arxiv preprint, Nov 2024

Summary Paper

We explore the trade-off between privacy and statistical utility in private two-sample testing under local differential privacy (LDP) for both multinomial and continuous data. We begin by addressing the multinomial case, where we introduce private permutation tests using practical privacy mechanisms such as Laplace, discrete Laplace, and Google’s RAPPOR. We then extend our multinomial approach to continuous data via binning and study its uniform separation rates under LDP over H}"older and Besov smoothness classes. The proposed tests for both discrete and continuous cases rigorously control the type I error for any finite sample size, strictly adhere to LDP constraints, and achieve minimax separation rates under LDP. The attained minimax rates reveal inherent privacy-utility trade-offs that are unavoidable in private testing. To address scenarios with unknown smoothness parameters in density testing, we propose an adaptive test based on a Bonferroni-type approach that ensures robust performance without prior knowledge of the smoothness parameters. We validate our theoretical findings with extensive numerical experiments and demonstrate the practical relevance and effectiveness of our proposed methods.

Imbalance

Weighted support vector machine for extremely imbalanced data

Jongmin Mun, Sungwan Bang, and Jaeoh Kim

Computational Statistics & Data Analysis, Mar 2025

Summary Paper

Data augmentation and loss function adjustments are two common techniques for imbalanced classification. In cases of extreme class imbalance, it is often standard practice to combine these two approaches. However, determining the optimal oversampling ratio and the degree of asymmetry in the loss function typically relies on heuristics. We further consider the scenario where the minority class consists of subgroups and propose a straightforward method for combining data augmentation and loss function adjustments. This method serves as a sample-based approximation of the population-level asymptotically Bayes optimal oracle procedure.

Brain

In-vivo integration of soft neural probes through high-resolution printing of liquid electronics on the cranium

Young-Geun Park, Yong Won Kwon, Chin Su Koh, Enji Kim, Dong Ha Lee, Sumin Kim, Jongmin Mun, Yeon-Mi Hong, Sanghoon Lee, Ju-Young Kim, Jae-Hyun Lee, Hyun Ho Jung, Jinwoo Cheon, Jin Woo Chang, and Jang-Ung Park

Nature Communications, Feb 2024

Summary Paper

Conventional neural probes are rigid, causing damages when implanted in freely moving animals. To address this issue, we present a soft, conformable neural interface system designed for long-term, stable monitoring of single-unit neural activity in freely moving subjects. This system integrates soft neural probes implanted in the brain with liquid metal-based electronics printed on the cranial surface. In-vivo mouse studies, employing dimensionality reduction, clustering analysis, and goodness-of-fit testing, provide statistical evidence of stable neural recordings over 33 weeks and behavior-induced activation across multiple brain regions during T-maze tests.