Dr. Edoardo M. Airoldi is the Millard E. Gladfelter Professor of Statistics and Data Science. He also serves as Director of the Fox School’s Data Science Center.
Airoldi joins the Fox School from Harvard University, where he had served since 2009 as a full-time faculty member in the Department of Statistics. He founded and directed the Harvard Laboratory for Applied Statistics & Data Science, until 2017. Additionally, he held visiting positions at MIT and Yale University, and served as a research associate at Princeton University.
A distinguished researcher, Airoldi has authored more than 140 publications and earned more than 12,000 citations. His work focuses on statistical theory and methods for designing and analyzing experiments on large networks and, more generally, modeling and inferential issues that arise in analyses that leverage network data.
His work has appeared in journals across statistics, computer science, and general science, including Annals of Statistics, Journal of the American Statistical Association, Journal of Machine Learning Research, Proceedings of the National Academy of Sciences, and Nature. He has received a Sloan Fellowship, the Shutzer Fellowship from the Radcliffe Institute of Advanced Studies, an NSF CAREER Award, and an ONR Young Investigator Program Award, among others. He has delivered a plenary talk at the National Academy of Sciences Sackler Colloquium on “Causal Inference and Big Data,” in 2015, and he has given an IMS Medallion Lecture at the Joint Statistical Meetings, in 2017.
Airoldi earned his PhD in Computer Science from Carnegie Mellon University, where he also received his Master of Science degrees in Statistics and Statistical and Computational Learning. He earned a Bachelor of Science in Mathematical Statistics and Economics from Italy’s Bocconi University.
- Methodology for the analysis of network data
- Design and analysis of experiments on large networks
- Geometry of inference in ill-posed inverse problems, including network tomography and contingency tables
- Modeling and inference of regulation and signaling dynamics, including sequencing and mass spectrometry
- Approximate inference strategies for data analysis at scale
- Ph.D. in Computer Science, Carnegie Mellon University, January 2007.
- M.S. in Computational and Statistical Learning, Carnegie Mellon University, August 2004.
- M.S. in Statistics, Carnegie Mellon University, August 2003.
- B.S. in Mathematical Statistics, Bocconi University, October 1999.
- 2013 – present, Associate Professor of Statistics, Harvard University
- September 2011 – present, Associate Faculty, The Broad Institute of MIT and Harvard
- January 2009 – December 2012, Assistant Professor of Statistics, Harvard University
- December 2006 – December 2008, Postdoctoral Fellow, Princeton University
P Toulis, EM Airoldi. “Asymptotic and finite-sample properties of estimators based on stochastic gradients”. Annals of Statistics. In press.
EM Airoldi, JM Bischof. “A regularization scheme on word occurrence rates that improves estimation and interpretation of topical content” (with discussion). Journal of the American Statistical Association. In press.
AW Blocker, EM Airoldi. “Template-based models for genome-wide analysis of next-generation sequencing data at base-pair resolution”. Journal of the American Statistical Association. In press.
S Lunagomez, S Mukherjee, R Wolpert, EM Airoldi. “Geometric representations of distributions on hypergraphs”. Journal of the American Statistical Association. In press.
AM Franks, G Csardi, DA Drummond, EM Airoldi. “Estimating a structured covariance matrix from multilab measurements in high-throughput biology”. Journal of the American Statistical Association, 110, 27-44, 2015.