Hi! I'm an assistant professor of Operations Research and Information Engineering (ORIE) at Cornell Tech, as part of the Jacobs Institute, and an ORIE, Computer Science, and Information Science field member at Cornell University.
Research overview
I advance computational understanding of and data-driven decision-making within societal systems. Methodologically, my work spans computer science, operations research, data science, and their intersection with economics and policymaking -- I try to combine the relative strengths of machine learning/AI and market design/operations to improve democracy, education, high-stakes recommenders, and societal systems at large. My recent work has been in two high-level directions:- AI and Operations for Public Interest: Developing empirical methods to audit and design governmental and other public interest systems, and how challenges such as missing data, uncertainty, and strategic behavior affect the design of these systems. This work is often in collaboration with government agencies or non-profits using real data. One focus has been improving resident crowdsourcing systems in cities, such as 311. Recent papers: Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing and A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing. See here for a recent talk video.
- Market design and Algorithms: Developing mathematical models for algorithmic systems in markets, especially how uncertainty affects the design of recommendation and matching in high-stakes settings such as labor and education. Recent papers: Monoculture in Matching Markets and Reconciling the accuracy-diversity trade-off in recommendations. See here for a recent talk video, which also overviews my work generally.
My work has received several awards, including NSF CAREER, 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. My research has been supported by the NSF, and Cornell Tech Urban Tech Hub, Meta, and Amazon Research awards.
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 work closely with several government agencies. I was senior scientific advisor to PredictWise, where I led data science efforts during the 2020 US election cycle. I am also involved with EAAMO (formerly MD4SG).
Contact me at ngarg@REMOVETHIScornell.REMOVETHISedu. Applicants: please read the information at the Contact page before emailing me.
What's new
| June 2025 | PhD Advisee Zhi Liu successfully defended his PhD! |
| April 2025 | Program co-chair for ACM EAAMO 2025! |
| August 2024 | Awarded NSF CAREER! |