Nikhil Garg

Hi! I'm an assistant professor of Operations Research and Information Engineering (ORIE) at Cornell Tech and the Technion, as part of the Jacobs Technion-Cornell Institute, and an ORIE, Computer Science, and Information Science field member at Cornell University.

My research is at the intersection of computer science, economics, and operations -- on the application of algorithms, data science/machine learning, and mechanism design to the study of democracy, online markets, and societal systems at large. I'm particularly interested in using these techniques to audit and design more equitable systems, and how challenges such as missing data, uncertainty, and strategic behavior affect the design of these systems.

Nowadays, I focus on studying urban systems and government services -- such as the 311 system (resident crowdsourcing), the library system, and in healthcare and education. In another line of work, I study recommendation and rating systems in high-stakes settings, and for example how they induce strategic behavior, and the role of diversity in recommendations. Other things I've worked on include gerrymandering, surge pricing, rating systems, how to vote on budgets, the role of testing in college admissions, stereotypes in word embeddings, and polarization on Twitter.

My work has received several awards, including the INFORMS George Dantzig Dissertation award, ACM SIGecom Dissertation Award (Honorable Mention), Forbes 30 under 30 for Science, and the NSF graduate research fellowship. My work has also been covered in the New York Times, Washington Post, Science Magazine, Smithsonian Magazine (in print), Stanford Engineering magazine, and Stanford News, among others.

I received a MS and PhD from Stanford in 2020, where I was lucky to be advised by Ashish Goel and Ramesh Johari and was part of the Stanford Crowdsourced Democracy Team and the Society and Algorithms Lab, after which I was a post-doc at UC Berkeley EECS. Before that, I graduated with a BS and BA from the University of Texas at Austin in 2015. I like to have first-hand practical experience in a domain before tackling research questions, and have built real systems (including for participatory budgeting and at NASA, Uber, and Upwork). I was senior scientific advisor to PredictWise, where I led data science efforts during the 2020 US election cycle. I am also involved with MD4SG, and most recently am co-creating a Junior Faculty working group.

Contact me at ngarg@REMOVETHIScornell.REMOVETHISedu.

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Cornell Students/PhD Applicants: I looking to work with PhD students, especially across ORIE, CS, IS, and Johnson. Please reach out if you think we may have shared interests (whether you are in Ithaca or NYC) -- my ideal student has strong technical skills (whether theoretical or empirical) and is excited to use them to study societal systems. If you have not yet been admitted, apply to one of the above departments and indicate your interest by noting my name in your application materials. I am an ORIE/CS/IS field member and so can serve as primary advisor to PhD students in those departments.

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