I am an assistant professor of computer science at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion and a computer science field member at Cornell University, with a secondary joint appointment as an Assistant Professor of Population Health Sciences at Weill Cornell Medical College. I develop data science and machine learning methods to study two broad areas: inequality and healthcare. For representative publications, please see my papers on inequality in pain (Nature Medicine, 2021); inequality in policing (Nature Human Behaviour, 2020); inequality in COVID-19 (Nature, 2021); segregation (Nature, 2023); and fair clinical prediction (New England Journal of Medicine, 2024). My work has been recognized by best paper awards at KDD and AISTATS, an NSF CAREER award, a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, MIT Technology Review 35 Innovators Under 35, Forbes 30 Under 30 in Science, AI2050 Early Career Fellowship, and Samsung AI Researcher of the Year. Here's my full CV and list of publications, and here's a professional bio and photo.


I am very lucky to get to work with many wonderful students and postdocs, some of whom are pictured on this page! I am actively looking for both PhD students and postdocs. If you are a student interested in working with me, please apply to Cornell for a CS PhD and mention my name in your application. (I also sometimes work with students from information science or other fields.) If you are interested in a postdoc, please shoot me an email directly.


Previously, I was a senior researcher at Microsoft Research New England and a PhD student in Jure Leskovec's lab at Stanford. Before my PhD, I did a master's in statistics at Oxford, and before that I spent a year as a data scientist at 23andMe and Coursera. I write a statistics blog, Obsession with Regression, and have also written for The New York Times, FiveThirtyEight, The Atlantic, The Washington Post, Wired, Times Higher Education, and various other publications. I always like hearing from people with cool ideas for things to do with data: shoot me an email at emma.pierson@cornell.edu!



What's new?


March 2024. Honored to have been awarded an AI2050 Early Career Fellowship!


January 2024. New paper: Accuracy and Equity in Clinical Risk Prediction. Pierson. New England Journal of Medicine, 2024. [Paper]


January 2024. New paper: Domain constraints improve risk prediction when outcome data is missing. Balachandar, Garg, and Pierson. To appear, ICLR, 2024. [Paper]


January 2024. New paper: Reconciling the accuracy-diversity trade-off in recommendations. Peng, Raghavan, Pierson, Kleinberg, and Garg. To appear, TheWebConf, 2024. [Paper]


December 2023. New paper: A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing. Agostini, Pierson*, and Garg*. To appear, AAAI, 2024. [Paper]


November 2023. New paper: Human mobility networks reveal increased segregation in large cities. Nilforoshan*, Looi*, Pierson*, Villanueva, Fishman, Chen, Sholar, Redbird, Grusky, and Leskovec. Nature, 2023. [Paper] [Press Briefing]


November 2023. Our Coursera class on algorithmic fairness, "Practical Steps for Building Fair Algorithms", is now available! The class is freely available to audit and designed to be accessible to everyone.


November 2023. Honored to have been named a Samsung AI Researcher of the Year, awarded to five early-career AI researchers worldwide!


August 2023. New paper: Coarse race data conceals disparities in clinical risk score performance. Movva*, Shanmugam*, Hou, Pathak, Guttag, Garg, and Pierson. Machine Learning for Healthcare Conference, 2023; best findings paper honorable mention, ML4H Symposium. [Paper] [Press coverage in The New York Times, Cornell News and Upturn newsletter]


July 2023. New paper: Topics, Authors, and Networks in Large Language Model Research: Trends from a Survey of 17K arXiv Papers. Movva*, Balachandar*, Peng*, Agostini*, Garg**, and Pierson**. Under review; New Directions in Analyzing Text as Data Meeting (TADA), 2023. [Paper] [Data Skeptic podcast episode]


June 2023. New paper: Detecting disparities in police deployments using dashcam data. Franchi, Zamfirescu-Pereira, Ju, and Pierson. FAccT, 2023. [Paper] [Code] [Press coverage in Cornell News and WNYC/Gothamist]