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.
Cornell Students/Applicants: I am an ORIE/CS/IS field member and so can be primary advisor to PhD students in those departments. If you have not yet been admitted, apply to one of the above departments and note my name in your application materials. My ideal student has strong technical skills (whether theoretical or empirical) and is excited to use them to study societal systems. If you are an undergraduate student at Cornell, I often host students through programs such as ELI and BURE, or for research course credit. I also often advise Master's students for independent study/Spec projects. While I do not currently have postdoc positions available, potential applicants may be interested in the following Cornell programs, through which I can be a mentor/collaborator: Center for Data Science for Enterprise and Society, Cornell Tech Runway Startup Postdoc Program, and Digital Life Initiative.
What's new
| Jan 2025 | Teaching Engineering Societal Systems this semester! |
| Jan 2025 | "Addressing Discretization-Induced Bias in Demographic Prediction" to appear in PNAS Nexus |
| Dec 2024 | New papers in CSCW`25 and AAAI`25! |
| September 2024 | "Monoculture in Matching Markets" and "User-item fairness tradeoffs in recommendations" accepted to NeurIPS`24, and "Ending Affirmative Action Harms Diversity Without Improving Academic Merit" will be at EAAMO`24! |
| August 2024 | Awarded NSF CAREER! |
| March-May 2024 | New projects online and accepted to FAccT and EC! |
| Jan 2024 | "Domain constraints improve risk prediction when outcome data is missing" accepted to ICLR`24!, and "Reconciling the accuracy-diversity trade-off in recommendations" accepted to WWW`24! |
| Dec 2023 | Two papers accepted to AAAI`24, both using Bayesian modeling to study and address disparities in government services! |
| Dec 2023 | "Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing" published in Nature Computational Science! |
| Sept 2023 | Announced as finalist for INFORMS Junior Faculty Interest Group Paper Award! |