SHORT BIO

Hello, everyone. I am currently a postdoctoral researcher in the Department of Industrial and Systems Engineering at Texas A&M University, working under the supervision of Prof. David J. Eckman. I earned my Ph.D. from the Department of Mathematical Sciences at Korea Advanced Institute of Science and Technology (KAIST) in August 2021. My advisor was Prof. Kyoung-Kuk Kim in the School of Management Engineering, KAIST.  Before joining Texas A&M University, I  was a postdoctoral researcher in the Department of Industrial and Manufacturing Engineering at Penn State University (from September 2021 to August 2022) and in the School of Industrial and Systems Engineering at Georgia Institute of Technology (from September 2022 to May 2023), both under the supervision of Prof. Eunhye Song. My research interests lie in a wide range of stochastic simulation, decision-making under uncertainty, and applied probability with applications to operations research.

[Curriculum Vitae] (Last update: Mar 6th, 2024)

I would welcome the opportunity to serve as a reviewer. Please don't hesitate to send me the request (I prefer the former among the email addresses below). 

CONTACT

• E-mail : thk5594 AT gmail DOT com or taeho.kim AT tamu DOT edu

EDUCATION

• 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 

RESEARCH INTERESTS

• Monte Carlo simulation methodologies

• Data-driven stochastic modeling and simulation analytics

• Advanced statistical methods in simulation analysis

• Risk-sensitive simulation optimization and stochastic optimization

PUBLICATIONS

• Rate-Optimal Budget Allocation for the Probability of Good Selection

Accepted to present at the Proceedings of the 2024 Winter Simulation Conference

with David J. Eckman

• Optimal Selection of Stochastic Alternatives for Tail Risk Mitigation

working paper

with Dohyun Ahn

• Optimizing Input Data Collection for Ranking and Selection

working paper

with Eunhye Song

• Risk-Sensitive Ordinal Optimization [link]

Proceedings of the 2023 Winter Simulation Conference

with Dohyun Ahn

• Optimizing Input Data Acquisition for Ranking and Selection: A View through the Most Probable Best [link]

Proceedings of the 2022 Winter Simulation Conference 

with Eunhye Song

• Selection of the Most Probable Best [link]

under second round major revision at Operations Research

with Kyoung-Kuk Kim and Eunhye Song

• Selection of the Most Probable Best under Input Uncertainty [link]

Proceedings of the 2021 Winter Simulation Conference

with Kyoung-Kuk Kim and Eunhye Song

• Ensemble Copula Coupling for Multivariate Input Modeling and Uncertainty Quantification

Reject and resubmission at Management Science

with Kyoung-Kuk Kim and Michael Fu

PRESENTATIONS

• 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.