I am currently a Visiting Researcher in the Core Data Science Group at Meta.
I obtained my Ph.D. from the Department of Industrial Engineering and Operations Research at Columbia University in 2021.
I am very fortunate to be advised by Yuri Faenza.
I am broadly interested in algorithms and mechanism designs for networked marketplaces and, more generally, in the interplay between economics, optimization, and computer science.
Much of my research so far has focused on matching markets, which have broad applications in various domains, such as school choice, kidney exchange, and refugee resettlement. The emphasis of my research is on designing practically efficient algorithms for two-sided matching markets utilizing structural properties from discrete mathematics, as well as optimization algorithms from computer science and operations research. I am also interested in policy aspects of mechanism designs, such as addressing fairness concerns (e.g., evaluation metrics, diversity) arising from school assignments in New York City.
I have also worked on dynamic network optimization problems under future uncertainty, which is oftentimes faced by, for instance, platform operators in marketplaces such as ride-hailing, lodging, and dating. My research here focuses on designing robust and efficient algorithms as well as providing theoretical guarantees for performance, using tools and concepts from statistical physics and dynamic programming.
Additionally, I am interested in and have worked on fundamental properties of combinatorial objects (e.g., knapsack polytope, stable matching polytope), from both algebraic and polyhedral perspectives.