I am a fourth-year PhD candidate in Operations Research at Georgia Tech working with Professor Alejandro Toriello. My research focuses on developing practical algorithms with theoretical guarantees for large-scale optimization, particularly in transportation and logistics. I am also broadly interested in decision making under uncertainty, especially for matching markets and online platforms. In summer of 2024, I interned in middle mile planning and routing optimization science team at Amazon and worked on driver break modeling in large-scale middle-mile routing problem. Prior to joining Georgia Tech, I received my BS and MS in Industrial and Management Engineering from Pohang University of Science and Technology (POSTECH), where I worked on scheduling and transportation problems in manufacturing and healthcare.
CV (last updated April 2025) Google scholar
Upcoming talks
- IISE Annual Conference & Expo 2025
- Presentation title: Recursive Partitioning and Batching for Network Design with Service Time Guarantees at Massive Scale
- Date and Location: TBD
Research Projects
- Asymptotic optimality of batching and greedy policies in dynamic matching (with Dr. Alejandro Toriello)
- We study a dynamic non-bipartite stochastic matching problem, motivated by ride-sharing and freight transportation marketplaces. We analyze the asymptotic optimality of batching and greedy policies and show how fast they converge to the optimal. Interestingly, both policies can achieve near-optimal performance with exponential convergence, even when considering impatient nodes.
- This work highlights how simple, well-structured policies can efficiently balance system performance and maximum waiting time.
- Dynamic matching under node impatience (with Dr. Alejandro Toriello and Dr. Diego Cifuentes)
- Our previous study shows that simple batching and greedy policies may not be optimal when nodes are highly impatient. We aim to develop a stronger relaxation model for a dynamic non-bipartite stochastic matching problem under node impatience.
- Recursive partitioning and batching for network design with service time guarantees at massive scale (with Dr. Alejandro Toriello and Dr. Alan Erera)
- We study a large-scale network design problem, motivated by parcel delivery industry. To handle the complexity of real-world instances, we propose a recursive graph partitioning and batching method, which breaks the problem into smaller regions and effectively merges the solutions for each subproblem.
- Our method finds high-quality solutions within hours for a real-world instance with over one million arcs and 40,000 commodities, while a commercial solver is unable to even build a model for a much smaller instance.