Nicole Lazar is a Professor of Statistics at Penn State.
Lazar received her PhD in Statistics from the University of Chicago in 1996. She received her MS in Statistics from Stanford University in 1993, and a BA in Psychology and Statistics (highest honors) from Tel Aviv University in 1988.
Her research interests include the foundations of statistical inference and the analysis of functional neuroimaging data. In particular, she has worked on fundamental inferential topics such as model selection, multiple testing problems, and likelihood theory, specifically in the context of modern large-scale data analysis problems. She has done pioneering work on the statistical analysis of cognitive neuroscience data, with a focus on functional magnetic resonance imaging (fMRI). Most recently, Lazar has been involved in the application of topological data analysis methods to scientific questions of interest in psychology and climatology. These techniques are at the interface of statistics, mathematics, and computer science, and exemplify her cross-disciplinary approach to research.
Lazar is the author of the book The Statistical Analysis of Functional MRI Data published by Springer. Her research has appeared in leading statistics and neuroscience journals. She is past Editor of The American Statistician and currently serves or has served as Associate Editor for several statistics journals. She was a co-Editor of a Special Issue of The American Statistician on Statistical Inference in the 21st Century: A World Beyond p<0.05.
She is an Elected member of the International Statistical Institute and a Fellow of the American Statistical Association. Lazar also served as President of the Caucus for Women in Statistics in 2019. She has an extensive record of service to the statistics profession. Prior to joining Penn State in 2020, she was on the faculty of Carnegie Mellon University and the University of Georgia, where she was Interim Department Head from 2014 to 2016.
Honors and Awards
Fellow of the Institute of Mathematical Statistics, 2021; Fellow, the American Statistical Association (2014); Elected Member, the International Statistical Institute (2006)
• Jaeger, A.P. and Lazar, N.A. (2020) Split sample empirical likelihood. Computational Statistics and Data Analysis, 150, article number 106994.
• Bedoui, A. and Lazar, N.A. (2020) Bayesian empirical likelihood for ridge and lasso regression. Computational Statistics and Data Analysis, 145, article number 106917.
• Wasserstein, R.L., Schirm, A.L. and Lazar, N.A. (2019) Moving to a world beyond "p<0.05." The American Statistician, 73(S1), 1--19.
• Moon, C., Giansiracusa, N. and Lazar, N.A. (2018) Persistence terrace for topological inference of point cloud data. Journal of Computational and Graphical Statistics, 27, 576--586.
• Brown, D.A., Datta, G.S. and Lazar, N.A. (2017) A Bayesian CAR model for correlated signal detection. Statistica Sinica, 27, 1125--1153.
• Ye, J., Li, Y., Lazar, N., Schaeffer, D. and McDowell, J. (2016) Finding common active regions in fMRI data from multiple subjects by periodogram clustering and clustering ensemble. Statistics in Medicine, 35, 2635--2651.
• Dasgupta, N., Lazar, N.A. and Genz, A. (2016) A look at multiplicity through misclassification. Sankhya, Series B, 78, 96--118.
• Terry, D.P., Sabatinelli, D., Puente, A.N., Lazar, N.A. and Miller, L.S. (2015) A meta-analysis of fMRI activation differences during episodic memory in Alzheimer's disease and mild cognitive impairment. Journal of Neuroimaging, 25, 849--860.
• Lazar, N. (2014) The Arts -- Digitized, Quantified, and Analyzed. The Best Writing on Mathematics, 2014, M. Pitici (ed.) Princeton University Press: Princeton, 96--104.
• Vexler, A., Kim, Y.M., Yu, J., Lazar, N.A. and Hutson, A.D (2014) Computing critical values of exact tests by incorporating Monte Carlo simulations combined with statistical tables. Scandinavian Journal of Statistics, 41, 1013--1030.
Fall 2020 - STAT 470W; Spring 2021 - STAT 512; Fall 2021- STAT 580/581; Spring 2022 - STAT 580/581, STAT 512