Pooja Kulkarni

Pooja Kulkarni is a postdoctoral scholar at the Data Science Institute, University of Chicago. Her research develops algorithms that make resource-allocation and market systems more predictable and stable. She studies this broad question through three interconnected directions. During her Ph.D. at UIUC, her work focused on fair allocation when agents have complex combinatorial preferences over sets of items. As a postdoctoral researcher at Northwestern University, she extended this agenda to online fair allocation, where agents or allocation requests arrive over time. More recently, her research has focused on the allocation, sharing, and pricing of modern digital goods such as data and API access to machine-learning models. Overall, her work lies at the intersection of algorithmic game theory, fair allocation, discrete optimization, and the economics of data and machine learning. Prior to joining her current postdoc, she did another short postdoc at Northwestern University. Before that, she completed her PhD from UIUC. Even before, she did her undergraduate from College of Engineering, Pune and a master’s from Indian Institute of Science. She was the gold-medalist at both of these places. Additionally, she has done varying length internships at NTT Data, Nvidia, and Meta.