Biography
I am a Postdoctoral Associate in the School of Civil and Environmental Engineering at Cornell University, working with Professor Linda Nozick. My research focuses on optimization methods for stochastic game-theoretic models in natural-hazard insurance and municipal land-use risk management.
Before joining Cornell, I was a Postdoctoral Researcher at the National Renewable Energy Laboratory (NREL) in the Artificial Intelligence, Learning, and Intelligent Systems (ALIS) group. I earned my Ph.D. in Industrial and Systems Engineering, with a minor in Mathematics, from North Carolina State University.
Broadly, my research centers on developing stochastic optimization algorithms for decision-making under uncertainty, with an emphasis on bridging theory and practice to design efficient and scalable methods that can be applied to real-world challenges across engineering and policy domains.
I am on the job market in 2025-2026.
Recent news:
- (Dec-6): Served as a session chair for a simulation optimization session and presented our multi-fidelity work at the 2025 Winter Simulation Conference (WSC).
- (Oct-25): Presented our work and served as a session chair in a simulation optimization session at the 2025 INFORMS Annual Meeting.
- (Aug-29): I will be joining Cornell University as a Postdoctoral Associate in the School of Civil and Environmental Engineering, working with Prof. Linda Nozick, starting in October 2025. My work will focus on developing optimization methods for stochastic game-theoretic models.
- (May-30): Our paper Complexity of Zeroth- and First-Order Stochastic Trust-Region Algorithms has been accepted for publication in the SIAM Journal on Optimization.
- (Mar-25): I am incredibly honored to be selected as the second-place winner of the 2025 Pritsker Doctoral Dissertation Award from the Institute of Industrial and Systems Engineers!
- (Jan-25): Our paper Adaptive Sampling-Based Bi-Fidelity Stochastic Trust Region Method for Derivative-Free Stochastic Optimization received major revisions from Mathematical Programming Computation.
- (Oct-24): Our paper Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms is accepted for publication in INFORMS Journal on Computing.
Awards and Achievements:
- Second Place Winner of the 2025 Pritsker Doctoral Dissertation Award, IISE (2025)
- Outstanding Reviewer Award, WSC (2024)
- ISE Distinguished Dissertation Award of the 2024 CA Anderson Awards, NCSU (2024)
- Travel Awards for the 2023 Annual Midwest Optimization Meeting, MSU (2023)
- Mentored Teaching Fellowship, NCSU (2022)
- Stochastic Optimization
- Quantum Computing
- Monte Carlo Simulation
- Decision-Focused Learning
PhD in Industrial Engineering, 2024
North Carolina State University
MS in Operations Research, 2021
North Carolina State University
MS in Logistics, 2017
Korea Aerospace University
BS in Logistics, 2015
Korea Aerospace University
Publications
Experience
- Developing simulation optimization methods for multi-region Cournot–Nash equilibrium problems in hurricaneinsurance markets, advancing computational frameworks for stochastic and nonlinear equilibrium analysis.
- Designed an adaptive sampling rule for multi-fidelity simulation oracles.
- Developed a novel stochastic trust region method for multi-fidelity stochastic optimization.
- Developed a second-order optimizer that uses diagonal Hessian approximations for deep learning applications.
- Developed a differentiable optimization algorithm for mixed-integer problems.
- Developed a subspace-based optimizer for large-scale traffic signal control by learning subspaces via deep RL.
- Analyzed the computational complexities with and without Common Random Numbers (CRN) in stochastic optimization and theoretically demonstrated that CRN can significantly reduce the computational burden.
- Enhanced the finite-time performance of the adaptive sampling trust-region method for simulation optimization through four key refinements:
- Improved the chances of identifying better solutions through the integration of direct search techniques.
- Constructed a quadratic model with diagonal Hessian within the trust region framework.
- Reused previously evaluated solutions and corresponding simulation outputs to reduce computational cost.
- Applied CRN to reduce the variance in function and gradient estimates.
- Showed that the refined algorithms converge to the first-order stationary point almost surely.
- Developed simulation optimization solvers and problems from scratch and tested them using Python (SimOpt)
- Developed a stochastic oracle for traffic signal control problems, analyzed its loss landscape characteristics, and evaluated the performance of various solvers in addressing the problem (Poster)
- Improved the randomized coordinate algorithm with adaptive sampling as a stochastic optimizer for variational hybrid quantum-classical algorithms.
- Designed a gaussian process based trust region algorithm for noisy derivative-free optimization problems.
Teaching
Instructor
ISE 362: Stochastic Models in Industrial Engineering
North Carolina State University (Fall 2023)
- Designed lecture materials, homework assignments, and exams.
- Lectured on probability, markov chain, and queueing systems.
Teaching Assistant
ISE 441: Introduction to Simulation, ISE 748: Quality Engineering
North Carolina State University (2019-2022)
- Assisted in courses such as stochastic modeling, simulation, optimization, and quality engineering.
- Held recitations, office hours, and managed grading.