About me

I am currently a Principal Researcher at the University of Chicago Booth School of Business, working with Baris Ata. I obtained my Ph.D. in Statistics from Cornell University, where I was advised by Jim Dai. From 2022 to 2024, I worked as an Applied Scientist II at amazon.com.

📢 I am on the academic job market in 2025–2026.

My research lies broadly in stochastic optimization and control, with an emphasis on stochastic networks and queueing theory. I study steady-state performance and diffusion control, particularly in heavy-traffic regimes. We pinoneered the notion of multi-scale heavy traffic and established the first product-form limits for generalized Jackson networks under this regime. I work on resource allocation in parallel-server systems, focusing on steady-state approximations with heterogeneous servers and class-dependent service rates.

I also work on stochastic matching in high-dimensional settings. I design dynamic matching policies through tractable approximations that balance matching value and congestion costs in large markets - such as ride-hailing and labor platforms - where high-dimensional control is essential and remains relatively underexplored in the literature.

A recent direction of interest is LLM inference viewed through the lens of stochastic networks. Modern LLM serving systems operate as large-scale service networks with batching, routing, and congestion under tight latency constraints. I am developing heavy-traffic approximations and queueing-based control to analyze and improve inference efficiency at scale.