Hello, everyone. I am currently a postdoctoral researcher at the HKUST Business School, Hong Kong University of Science and Technology (HKUST), working with Prof. Dongwook Shin. I earned my Ph.D. from the Department of Mathematical Sciences at Korea Advanced Institute of Science and Technology (KAIST) in August 2021. My PhD advisor was Prof. Kyoung-Kuk Kim, a professor in the College of Business, KAIST.
[Curriculum vitae] (Last update: Nov 1st, 2025)
E-mail : thk5594 AT gmail DOT com or taeho AT ust DOT hk
I would welcome an opportunity to serve as a reviewer. Please don't hesitate to send me the request.
RGC Junior Research Fellow Scheme (JRFS) 2025-2026, Hong Kong Research Grant Council (RGC) [list of awardees][link]
Project title: Reinforcement Learning with High-Dimensional Features (Supervisor: Dr. Dongwook Shin)
Award amount: HKD 420,000 per year
Ph.D., Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Sep. 2015 - Aug. 2021
(Thesis title: Data-driven simulation modeling, uncertainty quantification, and optimization)
B.Sc., Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Feb. 2011 - Aug. 2015
Monte Carlo methods in operations research and operations management
Sequential decision making under uncertainty
Data-driven stochastic modeling and simulation analytics
Advanced statistical methods in stochastic simulation
Data-Driven Sequential Sampling for Tail Risk Mitigation [arxiv]
submitted
with Dohyun Ahn
Ensemble Copula Coupling for Multivariate Input Modeling and Uncertainty Quantification
Major Revision at Mathematics of Operations Research
with Kyoung-Kuk Kim and Michael C. Fu
Optimizing Input Data Collection for Ranking and Selection [arxiv]
Major Revision at Operations Research
with Eunhye Song
Optimizing Input Data Acquisition for Ranking and Selection: A View through the Most Probable Best [doi] [full-paper]
Proceedings of the 2022 Winter Simulation Conference
with Eunhye Song
Rate-Optimal Budget Allocation for the Probability of Good Selection [doi] [full-paper]
Finalist, Best Contributed Theoretical Paper, WSC 2024
Proceedings of the 2024 Winter Simulation Conference
with David J. Eckman
Risk-Sensitive Ordinal Optimization [doi] [full-paper]
Proceedings of the 2023 Winter Simulation Conference
with Dohyun Ahn
Operations Research, 73(6):3199-3218, 2025
with Kyoung-Kuk Kim and Eunhye Song
Selection of the Most Probable Best under Input Uncertainty [doi] [full-paper]
Proceedings of the 2021 Winter Simulation Conference
with Kyoung-Kuk Kim and Eunhye Song
On Theoretical Analysis of Algorithmic Collusion
with Huijun Chen and Dongwook Shin
Reinforcement Learning with High-Dimensional Features
with Dohyun Ahn and Dongwook Shin
A Reduced Form Approach to Strategic Liquidity Provision in Automated Market Making
with Kyoung-Kuk Kim and Donghwa Seo
Ranking and Selection with Multiple Correct Answers
with Raahim Hashmi and David J. Eckman
Optimizing input data collection for ranking and selection, INFORMS Annual Meeting, Atlanta, GA, Oct 2025
Optimizing input data collection for ranking and selection, INFORMS International Meeting, Singapore, July 2025
Optimizing input data collection for ranking and selection, Department Seminar, UNIST IE, Mar 2025
Optimal input uncertainty reduction for ranking and selection, POMS-HK 2025, Hong Kong, Jan 2025
Optimal selection of financial strategies for tail risk mitigation, INFORMS Annual Meeting 2023, Phoenix, AZ.
Selection of the most probable best, INFORMS Annual Meeting 2022, Indianapolis, IN.
Data-driven stochastic modeling and uncertainty quantification via Ensemble Copula Coupling, OR Colloquium, Penn State IME, 2022
Selection of the most probable best under input uncertainty, 2021 Winter Simulation Conference, Phoenix, AZ.
Input-output analysis of high-dimensional stochastic systems, INFORMS Annual Meeting 2020, Virtual.
Multivariate input modeling and uncertainty quantification via Ensemble Copula Coupling, INFORMS Annual Meeting 2019, Seattle, WA.