In addition to the eight Emerging Leader Awards (see below), in 2024 COPSS is awarding the Presidents’ Award, the E. L. Scott Award and Lectureship, and the Distinguished Achievement Award and Lectureship. Read more about the winners:

2024 Distinguished Achievement Award and Lectureship: Robert Tibshirani

Robert Tibshirani is a Professor of Biomedical Data Science, and of Statistics, at Stanford University. He has made important contributions to the statistical analysis of complex datasets. Some of his best known contributions are the Lasso, which uses ℓ₁ penalization in regression and related problems, generalized additive models and Significance Analysis of Microarrays (SAM). He also co-authored five widely used 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 Medical School. Tibshirani received the COPSS Presidents’ Award in 1996. Given jointly by the world’s leading statistical societies, 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 of Britain in 2019. In 2021 he received the ISI Founders of Statistics Prize 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 E.L. Scott Award: Regina Liu

Regina Liu is currently Distinguished Professor of Statistics at Rutgers University. She received 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 FAA on aviation safety research projects on process control, text mining and risk management. Regina has served as editor for JASA and the Journal of Multivariate Analysis, and as Associate Editor for several journals, including JASA and the Annals of Statistics. She is an elected fellow of the ASA and the IMS, and was President of the IMS in 2020–21. 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 towards 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

Veronika Rockova is Professor of Econometrics and Statistics and the James S. Kemper Faculty Scholar at the Booth School of Business at the University of Chicago. 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 completed 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 datasets. 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, non-parametric Bayes, tree-based and deep-learning methods, high-dimensional inference, generative methods for Bayesian computation; for exemplary service to Statistics and for generous mentorship of students and post- doctoral researchers.

COPSS Emerging Leader Awards

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. For remarkable contributions to the statistical profession through mentorship of students and editorial services.

Bailey Fosdick, GTI Energy & 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.