The Committee of Presidents of Statistical Societies presents awards annually to honor statisticians who have made outstanding contributions to the profession. The following Emerging Leader Award winners were selected in addition to winners of the Presidents’ Award, Distinguished Achievement Award and Lectureship, and Elizabeth L. Scott Award. All awards will be presented at the 2024 Joint Statistical Meetings.
2024 Emerging Leader Award
Abhirup Datta
Johns Hopkins University Bloomberg School of Public Health
For fundamental methodological and theoretical contributions to geospatial statistics and machine learning with applications to the environmental and public health; for leading development and application of Bayesian methods for improving mortality estimates in low-and-middle-income countries; for prolific open-access software development; for being a role model in advising and mentoring of students and junior colleagues; and for service to the profession.
Anru Zhang
Duke University
For exceptional contributions to high-dimensional statistical inference, statistical learning theory, and particularly for groundbreaking work on statistical tensor learning; for significant contributions to medical informatics and nonconvex optimization; and for remarkable contributions to the statistical profession through mentorship of students and editorial services.
Bailey Fosdick
GTI Energy and Colorado School of Public Health
For impactful statistical contributions in the area of statistical network analysis methods; critical leadership for data-driven decision-making during the COVID-19 pandemic; and for commitment to and advocacy for a more just, equitable, diverse, and inclusive society.
Daniele Durante
Bocconi University
For cutting-edge scientific contributions to statistical modeling of graphs and to Bayesian theory and methods for categorical data, as well as exemplary service, dedicated mentoring, and creative outreach initiatives for early-career data scientists.
Jennifer Bobb
Kaiser Permanente Washington Health Research Institute
For significant methodological and applied contributions to the field of environmental biostatistics; for impactful research at the interface of cutting-edge statistical methods and real-world evidence to improve outcomes of people with substance use disorders; and for outstanding service to the profession.
Sandra Safo
University of Minnesota
For significant contributions to statistical and machine learning methods for integrative analysis; for dedication to education and mentoring; and for far-reaching services to the profession and society.
Shu Yang
North Carolina State University
For fundamental contributions to the development of trial design and analysis using real-world data and causal inference methods for complex clinical and observational studies; for outstanding advising and mentoring; and for a pivotal role in bridging the gap between academia and the pharmaceutical and regulatory sectors.
Zheng Tracy Ke
Harvard University
For pioneering contributions in statistical text analysis, especially optimal spectral algorithms for topic modeling; for outstanding contributions in developing statistical methods for complex network data, including mixed membership estimation and graph-cycle-count inference; for fundamental contributions in sparse inference and rare/weak signals; and for great services for the community such as organizing conferences and workshops and serving in various committees.
2024 Distinguished Achievement Award and Lectureship
Robert Tibshirani
Stanford University
Pre-Training and the Lasso
Wednesday, August 7, 4:00 p.m.
Robert Tibshirani is a biomedical data science and statistics professor at Stanford University. His contributions to the statistical analysis of complex data sets include the lasso—which uses L1 penalization in regression and related problems—generalized additive models, and significance analysis of microarrays. He also co-authored five books: Generalized Additive Models; An Introduction to the Bootstrap; The Elements of Statistical Learning; An Introduction to Statistical Learning; and Sparsity in Statistics: The Lasso and Its Generalizations. He is an active collaborator with many scientists at Stanford School of Medicine.
Tibshirani received the COPSS Presidents’ Award in 1996. The award recognizes outstanding contributions to statistics by a statistician under the age of 40. He was elected a fellow of the Royal Society of Canada in 2001, the National Academy of Sciences in 2012, and the Royal Society in in 2019. In 2021, he received the International Statistical Institute Founders of Statistics Prize for Contemporary Research Contributions for his 1996 paper “Regression Shrinkage and Selection via the Lasso.”
Citation: For fundamental contributions to statistics and machine learning that have deepened, broadened, and created a bridge between those fields; for bringing key statistical ideas in multiple testing and high-dimensional learning to the broader scientific community; for high-impact textbooks on generalized additive models, the bootstrap, high-dimensional statistics, and statistical learning that have come to define those fields; and for outstanding mentoring of PhD students and junior researchers.
2024 Elizabeth L. Scott Award
Regina Y. Liu
Rutgers University
Fusion Learning: Combining Inferences from Heterogeneous Data Sources Using Bootstrap, Depth, Confidence Distribution
Tuesday, August 6, 2:00 p.m.
Regina Liu is a distinguished professor of statistics at Rutgers University. She earned her PhD in statistics from Columbia University. Her research areas include data depth and broad geometric multivariate analysis, resampling, confidence distribution, and fusion learning in fusing inferences from diverse data sources. Aside from theoretical and methodological research, she has long collaborated with the Federal Aviation Administration on aviation safety research projects focusing on process control, text mining, and risk management. Liu has served as editor and associate editor for publications including the Journal of the American Statistical Association, Journal of Multivariate Analysis, and Annals of Statistics. She is an elected fellow of the ASA and Institute of Mathematical Statistics. She served as president of the IMS from 2020–2021. Among other distinctions, she is the recipient of the 2011 Stieltjes professorship from the Thomas Stieltjes Institute for Mathematics in the Netherlands and the 2021 ASA Noether Distinguished Scholar Award.
Citation: For her dedicated leadership and commitment to the statistical profession toward fostering opportunities, developing careers, and creating supportive work environment for underrepresented groups and new researchers and for her outstanding research contributions to statistics, particularly in data depth and nonparametric statistics.
2024 Presidents’ Award
Veronika Rockova
The University of Chicago
Veronika Rockova is professor of econometrics and statistics and the James S. Kemper Faculty Scholar at The University of Chicago Booth School of Business. She joined Booth after completing her postdoctoral training in statistics at the Wharton School of the University of Pennsylvania. She earned a bachelor’s degree in mathematics and a master’s degree in mathematical statistics from Charles University in Prague. Subsequently, she pursued a master’s degree in biostatistics at Hasselt University in Belgium and later earned her doctoral degree in biostatistics at Erasmus University in Rotterdam. Her research interests lie at the intersection of statistics and machine learning, with a primary focus on creating innovative decision-centric tools for extracting insights from extensive data sets. She specializes in Bayesian computation, variable selection, high-dimensional decision theory, and hierarchical modeling.
Citation: For path-breaking contributions to theory and methodology at the intersection of Bayesian and frequentist statistics in the areas of variable selection, factor models, nonparametric Bayes, tree-based and deep-learning methods, high-dimensional inference, and generative methods for Bayesian computation and for exemplary service to statistics and for generous mentorship of students and postdoctoral researchers.
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