I am a Research Scientist at Amazon’s Supply Chain Optimization Technologies (SCOT) organization. I develop optimization models and tools that enhance the decision-making process in inventory procurement and placement decisions in Amazon’s global fulfillment network. I earned my Ph.D. in Operations Research & Industrial Engineering at the University of Texas at Austin. Before that, I attended the University of Waterloo in Canada and got my Bachelor’s degree in Computer Science & Statistics.
My research interests include online algorithms, optimization under uncertainty, algorithmic game theory as well as data analytics and statistical modeling. During my Ph.D., I applied these methodologies to a range of real-world problems, particularly in electricity markets and neuroscience.
NEWS
[09/24] Our paper “I.I.D. Prophet Inequality with a Single Data Point” is under Minor Revision at Artificial Intelligence.
[12/23] Our paper “Eliciting Truthful Reports with Partial Signals in Repeated Games” is accepted to appear in Theoretical Computer Science.
[10/23] Our paper “BMRMM: An R Package for Bayesian Markov (Renewal) Mixed Models” is accepted to appear in The R Journal.
[09/23] Our paper “Prophet Inequality on I.I.D. Distributions: Beating 1-1/e with a Single Query” has been accepted to appear at the WINE’23 conference.
[07/23] I started working as a full-time Research Scientist @ Amazon.
[04/23] I have defended my Ph.D. dissertation on Online Optimization and Decision-Making Problems under Uncertainty in the Power Systems!!