Researchers from Cornell Tech have developed a method to identify delays in the reporting of incidents such as downed trees and power lines, which could lead to practical insights and interventions... Read more about Method may improve cities’ responses to resident service calls
Nikhil Garg joined the Cornell University faculty as an Assistant Professor of Operations Research and Information Engineering at Cornell Tech in July 2021.
Garg’s research is at the intersection of computer science, economics, and operations—on the application of algorithms, data science, and mechanism design to the study of democracy, markets, and societal systems at large. His research interests include surge pricing, rating systems, how to vote on budgets, the role of testing in college admissions, stereotypes in word embeddings, and polarization on Twitter.
Garg received his Ph.D. from Stanford University in 2020, where he was part of the Society and Algorithms Lab and Stanford Crowdsourced Democracy Team. He also received a B.S. and B.A. degrees from the University of Texas at Austin in 2015.
He has spent time at Uber, NASA, Microsoft, the Texas Senate, and IEEE's policy arm, and most recently was the principal data scientist at PredictWise—which provides election analytics for political campaigns—and is currently completing a postdoc at the University of California, Berkeley in the Department of Electrical Engineering and Computer Science.
- Nikhil Garg and Hamid Nazerzadeh.“Driver Surge Pricing” (2021). Management Science, Accepted.
- Nikhil Garg, Ashish Goel, and Ben Plaut. “Markets for Public Decision-making” (2020). Social Choice and Welfare.
- Nikhil Garg and Ramesh Johari. “Designing Informative Rating Systems: Evidence from an Online Labor Market” (2020). Manufacturing & Service Operations Management.
- Nikhil Garg, Vijay Kamble,Ashish Goel, David Marn, and Kamesh Munagala. “Iterative Local Voting for Collective Decision-making in Continuous Spaces” (2019). Journal of Artificial Intelligence Research.
- Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou.“Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes” (2018). Proceedings of the National Academy of Sciences.
Selected Awards and Honors
- INFORMS George Dantzig Dissertation Award, 2020
- M&SOM Student Paper Award (2nd place), 2020
- National Science Foundation Graduate Research Fellowship, 2015-2020
- Stanford McCoy Center for Ethics in Society Graduate Fellow, 2017-2018
- Virginia & Ernest Cockrell, Jr. Scholarship in Engineering (4 year full scholarship), 2011-2015
B.S. (Electrical and Computer Engineering), University of Texas at Austin, 2015
B.A. (Electrical Engineering), University of Texas at Austin, 2015
Ph.D. (Electrical Engineering), Stanford University, 2020