معرفی
Nikhil Garg is an Assistant Professor of Operations Research and Information Engineering at Cornell Tech, Cornell University, since July 2021. He holds a Ph.D. in Electrical Engineering from Stanford University (2020) and dual B.S./B.A. degrees in Electrical and Computer Engineering from the University of Texas at Austin (2015). His research bridges computer science, economics, and operations research, focusing on algorithms, data science, and mechanism design for societal systems like democracy, markets, and public policy.
Key research interests include surge pricing mechanisms, rating system design, voting systems, and addressing biases in AI (e.g., stereotypes in word embeddings). He has held roles at Uber, NASA, Microsoft, and the Texas Senate, and previously served as Principal Data Scientist at PredictWise. He is also completing a postdoctoral fellowship at UC Berkeley’s Department of Electrical Engineering and Computer Science.
His work has received recognition such as the NSF CAREER Award (2024), INFORMS George Dantzig Dissertation Award (2020), and M&SOM Student Paper Award (2020). Research outputs span topics like fair recommendation systems, equitable congestion pricing, and analyzing polarization in social media.
Recent projects emphasize improving public decision-making through statistical methods that address information gaps and heterogeneous behavior. His NSF-funded work aims to develop transparent, efficient systems for public interest applications.
