Seungki Min

About Me

I am an Assistant Professor at Seoul National University (SNU) Business School, specializing in Operations Management. I am very interested in sequential decision-making and learning algorithms for modern business applications, with a particular preference on dynamic programming approaches with Bayesian perspectives (e.g., Thompson sampling).

I earned my undergraduate degree in Electrical Engineering from SNU and my Ph.D. in Business from Columbia University. After completing my Ph.D., I began my academic career as an Assistant Professor in the Industrial and Systems Engineering Department at KAIST before recently joining the Operations Management group at SNU Business School. Prior to academia, I spent over 6 years as a quant developer in the financial industry. At the intersection of engineering and business, I am passionate about designing algorithms that perform effectively in practice while uncovering the managerial insights that drive algorithmic innovations.

Papers

• S. Kim, D. Ahn, S. Min. “On the Optimality of Tracking Fisher Information in Adaptive Testing with Stochastic Binary Responses.”, Submitted. Initial version: 2025

• S. Choi, C. Kwon, S. Min. “Dynamic Pricing of an Expiring Item under Strategic Buyers with Stochastic Arrival.”, Submitted. Initial version: 2025

• S. Min, D. J. Russo. “An Information-Theoretic Analysis of Nonstationary Bandit Learning.” Major revision at Operations Research. Initial version: July 2023
  • Preliminary version: S. Min, D. J. Russo. “An Information-Theoretic Analysis of Nonstationary Bandit Learning.” Proceedings of the 40th International Conference on Machine Learning (ICML), PMLR 202:24831-24849, 2023
• S. Min, C. Maglaras, C. C. Moallemi. “Thompson Sampling with Information Relaxation Penalties.” Management Science 71(3):1865–1888, 2025
  • Preliminary version: S. Min, C. Maglaras, C. C. Moallemi. “Thompson Sampling with Information Relaxation Penalties.” In Advances in Neural Information Processing Systems 32 (NeurIPS), pages 3549–3558, 2019
• Y. Kanoria, S. Min, P. Qian. “The Competition for Partners in Matching Markets.” Management Science 71(4):5230–5250, 2025
  • Preliminary version: Y. Kanoria, S. Min, P. Qian. “In Which Matching Markets does the Short Side Enjoy an Advantage?” Proceedings of the Thirty-Second Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 1374–1386, March 2021

• J. Kim, S. Min. “Risk-sensitive Policy Optimization via Predictive CVaR Policy Gradient.” Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR 235:24354-24369, 2024

• S. Min, C. Maglaras, C. C. Moallemi. “Cross-sectional Variation of Intraday Liquidity, Cross-Impact and their Effect on Portfolio Execution.” Operations Research 70(2):830–846, March 2022

• S. Min, C. Maglaras, C. C. Moallemi. “Risk-sensitive Optimal Execution via a Conditional Value-at-Risk Objective.” Major revision at Management Science. Initial version: 2022. 2021 INFORMS Section on Finance Best Student Paper Competition Finalist

• S. Min, C. C. Moallemi, D. J. Russo. “Policy Gradient Optimization of Thompson Sampling Policies.” Major revision at INFORMS Journal on Computing. Initial version: 2020

Work Experience

J.P. Morgan, New York, U.S. (July 2019 – Sep 2019)

• Research internship, Automated Trading System
  • Conducted research on high-frequency price impact and high-frequency execution strategy

Tachyon Trading, Seoul, South Korea (May 2012 – Jun 2015)

• Co-founder & Head of IT, High-frequency trading & market making
  • Developed trading strategies for Kospi200 & Nikkei index futures and options
  • Developed a low-latency trading platform including simulation/analysis tools

Yonhap Infomax, Seoul, South Korea (Feb 2009 – Dec 2011)

• Developer, Financial market data vendor & news agency
  • Served alternative military service
  • Developed financial data visualization/analysis tools & mobile apps

Teaching

• Operations Research: Stochastic Modeling (Fall 2021, Fall 2022, Fall 2023, Fall 2024)
• Data Science for Decision Making (Spring 2023, Spring 2024)
• Basics of Artificial Intelligence (Fall 2021, Fall 2022, Fall 2023)
• Data-driven Decision Making and Control (Spring 2022)