September 29 – October 1, 2023


George W. Snedecor Memorial Distinguished Lecture – Xiao-Li Meng, Harvard University

Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review, is well-known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files,” a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (2004-2012) and the Dean of Graduate School of Arts and Sciences (2012-2017). He was elected a fellow of the IMS in 1997, fellow of the American Statistical Association in 2004,  fellow of the American Academy of Arts and Sciences in 2020

Friday, September 29, 2023 9:00-10:00 am.


Henry A. Wallace Memorial Distinguished Lecture – Karen Kafadar, University of Virginia
Title of talk: Statistics in 2023: The critical role of Robust Statistical Methods for Big Data

Karen Kafadar is Commonwealth Professor and former Chair of Statistics at University of Virginia. Her research focuses on statistical methods and data analysis in the physical, chemical, biological, and engineering sciences.  She received her BS and MS from Stanford and her PhD from Princeton and has held positions at NIST, Hewlett Packard, National Cancer Institute, University of Colorado-Denver and Indiana University. She has co-authored several reports for the National Academy of Sciences, including Strengthening Forensic Science in the U.S. (2009) and the Anthrax Investigation (2011).  Her recent work concerns statistical methodology for randomized cancer screening trials, and classification and estimating error rates in eyewitness identification and forensic science.  She is past Editor of The Journal of American Statistical Association (ASA) Reviews, Technometrics, and The Annals of Applied Statistics; 2012 IASC President; co-PI on the NIST-funded Center for Statistical Applications in Forensic Science, and was the 2019 President of the American Statistical Association. She is an Elected Fellow of the American Association for the Advancement of Science (AAAS), the ASA and an Elected Member of the International Statistical Institute.



Friday, September 29, 2023 12:20-1:20 pm.


Henderson Memorial Distinguished Lecture – Rina Foygel Barber, University of Chicago
Title of talk: Recent results in distribution-free predictive inference:  the jackknife+ and algorithmic stability

 Rina Foygel Barber is the Louis Block Professor of Statistics at the University of Chicago, where she has been faculty since Jan. 2014. Prior to joining the faculty, she was a NSF postdoctoral fellow at Stanford University advised by Emmanuel Candes, and received her PhD in Statistics at University of Chicago in 2012 advised by Mathias Drton and Nathan Srebro. Rina’s research focuses on developing theory and methodology for statistical problems in challenging modern settings, including distribution-free inference, high-dimensional multiple testing, and sparse and low-rank estimation, as well as nonconvex optimization with applications in medical imaging. Her research has been recognized by awards including the Commmittee of Presidents of Statistics Societies (COPSS) Presidents’ Award (2020), the Peter Gavin Hall Institute of Mathematical Statistics (IMS) Early Career Prize (2020), and the IMS Medallion Lecture and Award (2022). She was elected as a Fellow of the IMS in 2023.



Friday, September 29, 2023 3:00-4:00 pm.


Wayne A. Fuller Distinguished Lecture – Eric Tchetgen Tchetgen, University of Pennsylvania
Title of talk: Introducing the Forster-Warmuth Nonparametric Counterfactual Regression

Eric J. Tchetgen Tchetgen is University Professor, Professor of Biostatistics at the Perelman School of Medicine and Professor of Statistics and Data Science at The Wharton School at the University of Pennsylvania. He co-directs the Penn Center for Causal Inference, which supports the development and dissemination of causal inference methods in Health and Social Sciences.  He has published extensively on Causal Inference, Missing Data and Semiparametric Theory with several impactful applications ranging from HIV research, Genetic Epidemiology, Environmental Health and Alzheimer’s Disease and related aging disorders. He is an Amazon scholar working with Amazon scientists on a variety of causal inference problems in the Tech industry space.  Professor Tchetgen Tchetgen is an 2022 inaugural co-recipient of the newly established Rousseeuw Prize for statistics in recognition for his work in Causal Inference with applications in Medicine and Public Health.



Saturday, September 29, 2023 8:30-9:30 am.


Distinguished Alumnal Lecture — Scott Vander Wiel, Los Alamos National Laboratory
Title of talk: Functional Nuclear Data — Estimating and Propagating Uncertain Functions

Scott Vander Wiel is a fellow of the American Statistical Association, conducting research at Los Alamos National Laboratory since 2005 and previously at Bell Laboratories since 1991. He leads collaborations with scientists and engineers to solve strategic problems at the core mission

of the U.S. Nuclear Weapons Complex. His work shapes assessments of the Nation’s aging stockpile as reported annually to the president. Scott analyzes data and develops statistical methods for problems in diverse areas such as weapon physics simulations, turbulence, atomic nuclear data, material microstructure, nuclear forensics, Doppler velocimetry, thermodynamic equations of state, radio astronomy, malware detection, power grid uncertainty, and weapon response safety. He holds patents on methods for network traffic modeling and for incremental quantile estimation. His Ph.D. is in Statistics from Iowa State University.



Saturday, September 29, 2023 11:30 am -12:30 pm.


Herbert A. David Memorial Distinguished Lecture – Soumendra N. Lahiri, Washington University
Title of talk: On the validity of the Central Limit Theorem in high dimensions

Soumendra N. Lahiri is Stanley A. Sawyer Professor in Mathematics and Statistics at Washington University, Saint Louis. He is a statistician by training and received his PhD from Michigan State University in 1989. He has worked in multiple areas of Statistics and Data Science:  He has

done theoretical and methodological work on Asymptotic expansions, High Dimensional Data, Machine Learning Resampling methods, Spatial Statistics, Time Series, and on their applications to Astronomy, Climatology, Computational Social Science, and Neuroscience. He has authored two books and more than 125 research papers. He is an elected fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics (IMS) and an elected member of the International Statistical Institute (ISI). He has been on the faculty of Iowa State University, Texas A&M University, and NC State University in the past.



Saturday, September 29, 2023 4:30 -5:30 pm.


75th Anniversary Conference Distinguished Lecture – Hal S. Stern, University of California, Irvine
Title of talk: A Rose by Any Other Name: Statistics, Machine Learning and Artificial Intelligence 

Hal Stern is Provost and Executive Vice Chancellor at the University of California, Irvine (UCI) and Chancellor’s Professor in the Department of Statistics. Provost Stern previously served UCI as founding chair of the Department of Statistics, Dean of the Donald Bren School of Information and Computer Sciences, and Vice Provost for Academic Planning. Prior to joining the faculty at UCI, he held faculty positions at Iowa State University and Harvard University. Within the field of statistics, Stern is known for his research on Bayesian statistical methods and for collaborative projects in the life sciences and social sciences. Current areas of interest include applications of statistics in forensic science and in psychiatry and human behavior. He is co-director of the Center for Statistics and Applications in Forensic Evidence, funded by the National Institute of Standards and Technology. Projects in the Center include statistical methods for analysis of footwear impression and bloodstain pattern evidence. He is also part of the leadership team for the Conte Center at UCI, funded by the National Institute of Mental Health, which is studying how early-life experiences, and especially early-life adversity, influence brain maturation and contribute to vulnerability to mental health problems throughout life. Stern is a fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute for Mathematical Statistics. He earned a B.S. in mathematics from the Massachusetts Institute of Technology and a M.S. and Ph.D. in statistics from Stanford University.

Saturday, September 29, 2023 7:30 -8:30 pm.


Laurence H. Baker Memorial Distinguished Lecture – Kenneth Lange, University of California, Los Angeles
Title of talk: Examples of MM Algorithms

Kenneth Lange is the Maxine and Eugene Rosenfeld Endowed Professor of Computational Genetics in the Departments of Computational Medicine, Human Genetics, and Statistics at the University of California, Los Angeles (UCLA). He has served as Chair of  the UCLA Department of Computational Medicine, and as Chair of the Department of Human Genetics. From 1994 to 1998 he was Professor of Biostatistics and Mathematics and the Pharmacia & Upjohn Foundation Research Professor at the University of Michigan. During his academic career, he has mentored 22 doctoral students and 8 postdoctoral fellows and authored six advanced textbooks. Lange was born in Angola, Indiana, and raised in the neighboring town of Auburn, Indiana. As an undergraduate, he attended Case Institute of Technology and graduated from Michigan State University with a degree in mathematics in 1967. He graduated from MIT in 1971 with a PhD in mathematics. Lange’s research interests include genetic epidemiology, population genetics, membrane physiology, infectious disease modeling, demography, oncology, medical imaging, stochastic processes, optimization theory, and computational statistics. Many of his landmark papers predate by a decade or more the current flood of biological applications of hidden Markov models, Markov chain Monte Carlo (MCMC), and high-dimensional optimization. His scientific achievements include: a) earliest algorithm for calculating Mendelian likelihoods over inbred pedigrees, b) earliest application of linear mixed models to human pedigree data, c) introduction of EM (expectation-maximization) and MM (majorization-minimization) algorithms to medical imaging, d) early application of MCMC sampling in human genetics, e) construction of accurate statistical models for gene mapping by radiation hybrids, f) introduction of lasso penalized regression to genome-wide associationstudies (GWAS), g) introduction of hidden Markov models for single channel recording experiments in physiology, h) introduction of graphical processing units (GPUs) to high-performance statistical computing, and i) popularization and generalization of the MM principle in computational statistics. Lange and his UCLA colleagues wrote the software programs SimWalk, Mendel, and Admixture, widely used and freely distributed to the scientific community. In 2019, Lange and colleagues launched the OpenMendel project for cooperative software development in genomics.

Lange won the Committee of Presidents of Statistical Societies (COPSS) Snedecor award from the Joint Statistical Societies in 1993 and the Arno Motulsky-Barton Childs Award from the American Society of Human Genetics in 2020. He was elected a Fellow of the American Statistical Association (ASA) in 2011, and of the Institute of Mathematical Statistics (IMS) in 2012, for “groundbreaking developments in statistical computing and statistical genetics as a prolific and rigorous scholar and mentor.” Kenneth Lange was elected   Fellow of the American Association for the Advancement of Science (AAAS) for distinguished contributions to the fields of medicine and gerontology, particularly to the understanding of the risk factors and societal burden of Alzheimer’s and dementia. He was elected to the US National Academy of Sciences in 2021.

Sunday, October 1, 2023 10:00-11:00 am


George F. Zyskind Distinguished Lecture – Emmanuel Candès, Stanford University
Title of talk:  Model-free selective inference

Emmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, and Professor of Electrical Engineering (by courtesy). His research interests lie at the interface of statistics, information theory,  signal processing and computational mathematics. He received his Ph.D. in statistics from Stanford University in 1998. Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by NSF to early-career scientists, and the MacArthur Fellowship, popularly known as the ‘genius award’. He has given over 80 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was awarded the George Polya Prize awarded by the Society of Industrial and Applied Mathematics (SIAM) (2010), the Collatz Prize from the International Council for Industrial and Applied Mathematics (2011), the Lagrange Prize in Continuous Optimization from the Mathematical Optimization Society (MOS) and SIAM (2012), the Dannie Heineman Prize presented by the Academy of Sciences at Göttingen (2013), the AMS-SIAM George David Birkhoff Prize in Applied Mathematics (2015), the Prix Pierre Simon de Laplace from the Société Française de Statistique (2016), the Ralph E. Kleinman Prize from SIAM (2017). He was selected as the Wald Memorial Lecturer by the Institute of Mathematical Statistics (2017).  He received the 2020 Princess of Asturias Award for Technical and Scientific Research. The IEEE Board of Directors selected him along with Terence Tao and Justin Romberg to receive the 2021 IEEE Jack S. Kilby Signal Processing Medal. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014.