In the last issue, we briefly announced the winners of this year’s Committee of Presidents of Statistical Societies (COPSS) awards. Here’s some more background on them all:

Alicia Carriquiry to deliver the 2021 Florence Nightingale David Award Lecture

Nancy Gordon (Chair of the Award Committee) and Daniel Nettleton (Iowa State University) write:

The Committee of Presidents of Statistical Societies (COPSS) has selected Alicia Carriquiry, President’s Chair in Statistics at Iowa State University, to be the 2021 COPSS Florence Nightingale David Award and Lectureship. Professor Carriquiry will receive the award during the 2021 Joint Statistical Meeting, where she will also deliver the F.N. David Lecture titled “Statistics in the Pursuit of Justice: A More Principled Strategy to Analyze Forensic Evidence.” The F.N. David Award, sponsored jointly by COPSS and the Caucus for Women in Statistics, recognizes Carriquiry for her contributions to the profession that have spanned over 30 years.

Carriquiry researches applications of statistics in human nutrition, bioinformatics, forensic sciences and traffic safety and has published over 140 peer-reviewed articles in academic journals. Carriquiry’s research has been used to make a difference in the world. She has worked with various government and health agencies around the world to improve health and nutrition, including the National Center for Health Statistics, the National Institutes of Health, the European Union and the World Health Organization.

For the past six years, Carriquiry has been the director of the Center for Statistics and Applications in Forensic Evidence (CSAFE), a National Institute of Standards and Technology (NIST) Center for Excellence. With more than 80 researchers from across six universities, CSAFE is developing statistically sound and scientifically solid methods to analyze and interpret evidence, providing the forensic community with accessible tools, open-source databases and educational opportunities.

Carriquiry has published cutting-edge work on source matching for bullet markings, glass fragments, footwear impressions and handwriting analysis. Her work potentially could have a significant and beneficial impact on the U.S. criminal justice system.

Carriquiry received a master’s degree in animal science from the University of Illinois and a master’s and doctoral degree in statistics and animal genetics from Iowa State University. She joined the Iowa State faculty in 1990 and has held various leadership roles at the university.

As the first female faculty member promoted to full professor in the Department of Statistics at Iowa State University, Carriquiry continues to advocate for female and early-career faculty by providing them with opportunities for success. The Department now has 15 other female faculty members who have benefited from the path Dr. Carriquiry blazed before them and from her subsequent advocacy and support.

Carriquiry is a fellow of several statistical associations, including the ASA, ISI, IMS and ISBA. She is an elected member of the National Academy of Medicine and a fellow of the American Association for the Advancement of Science. She joined the Intelligence Science and Technology Experts Group of the US National Academies. In 2018, Carriquiry became a technical advisor for the Association of Firearm and Tool Mark Examiners, and in 2020 was elected an associate member of the American Academy of Forensic Sciences.


Wing Hung Wong to deliver the 2021 COPSS Distinguished Achievement Award Lecture

Daniela Witten (University of Washington, Chair of the Award Committee) writes:

COPSS has selected Professor Wing Hung Wong to be the recipient of the 2021 COPSS Distinguished Achievement Award and Lectureship (DAAL). Professor Wong will give the COPSS Distinguished Lecture at the 2021 Joint Statistical Meetings.

Professor Wong serves on the faculty of Stanford University, where he is currently Professor of Statistics, Professor of Biomedical Data Science, and holder of the Stephen R. Pierce Family Goldman Sachs Professorship in Science & Human Health. Before joining the Stanford faculty in 2004, he held teaching positions at the University of Chicago, The Chinese University of Hong Kong, UCLA, and Harvard University. He chaired the Stanford Department of Statistics from 2009 to 2012.

His research contributions include mathematical statistics, where he clarified the large sample properties of sieve maximum likelihood estimates in general spaces; Bayesian statistics, where he introduced sampling-based algorithms into Bayesian computational inference; and computational biology, where he developed tools for the analysis of microarrays and sequencing data, and applied them to study gene regulatory systems.

Professor Wong was the winner of the COPSS Presidents’ Award in 1993. He was elected to the National Academy of Sciences in 2009 and the Academia Sinica in 2010. He was a founding member of the Hong Kong Academy of Sciences in 2015.

As the recipient of the 2021 COPSS DAAL, Professor Wong will give a talk at the 2021 Joint Statistical Meetings titled “Understanding human trait variation from the gene regulatory systems perspective.” Genome-wide association studies have shown great success in identifying genetic loci relevant to a number of human traits, such as disease susceptibility and anthropometric features. However, such direct statistical associations provide limited information on the underlying biological processes relevant to the trait. Professor Wong will argue that the integration of gene regulatory information is critical to achieving a better understanding of these genotype-phenotype relations. He will review research by his lab and others on the inference of context-specific gene regulatory relations based on bulk or single cell data from diverse cell types, tissue types, and developmental contexts. He will also describe his lab’s ongoing efforts to exploit this information to build multi-layer statistical models capable of providing a more mechanistic understanding of human trait variation.


COPSS George W. Snedecor Award Winner: David Dunson

Kerrie Mengersen (Queensland University of Technology, Chair of the Award Committee) and Sudipto Banerjee (University of California, Los Angeles) write:

COPSS has selected Professor David B. Dunson, currently Arts & Sciences Distinguished Professor in the Department of Statistical Sciences at Duke University, to be the recipient of the 2021 George W. Snedecor Award.

This award, established in 1976, honors an individual who was instrumental in the development of statistical theory in biometry and recognizes a noteworthy publication within three years of the date of the award. The award, given biennially (odd years) since 1991, consists of a plaque and a cash honorarium of $2,000 and is presented at the Joint Statistical Meetings. The recognized publication is: Miller, J.W. and Dunson, D.B. (2019). Robust Bayesian inference via coarsening. Journal of the American Statistical Association, 114, 1113–1125. DOI:

David Dunson has maintained an astounding research portfolio throughout his career with over 400 peer-reviewed scholarly manuscripts appearing in leading journals, and co-authored a leading textbook on Bayesian statistical science. Interpreting the field of biometry in the broader sense as that of quantitative methods in biological and health sciences, about 120 of David’s papers in the top echelon of journals in our profession have been directly instrumental in advancing statistical theory related to biometry. His scholarly manuscripts, without exception, tackle the many challenging curiosities in modern science by developing theoretically rigorous statistical frameworks, stochastic process models, and computational algorithms for the complex and high-dimensional data generated in scientific laboratories across a variety of scientific disciplines.

In the recognized publication, Miller and Dunson (2019) offer a very innovative and fundamentally different approach to Bayesian inference that is based upon the idea of “coarsening” and is referred to as “c-Bayes’’. Briefly, rather than conducting inference based upon the usual posterior distribution of the parameters conditional on the event that the data has been generated from a posited model, c-Bayes conditions on the model-generated data being a sufficiently close approximation to the observed data. Miller and Dunson’s c-Bayes approach has proven particularly potent in consolidating robustness of inference against perturbations from misspecified and dubious modeling assumptions. The manuscript adeptly elucidates the underlying theoretical issues surrounding bias, calibration, measurement error, over-dispersion, and over-fitting. Miller and Dunson offer impressive novelty in theory, methods, and computation.

A particularly appealing example in biometry that is presented in the paper applies c-Bayes for robust clustering in flow cytometry — a high-throughput technology for detecting and measuring physical and chemical characteristics of a population of cells or particles. Traditionally, this clustering is performed manually by defining piecewise linear boundaries between regions using one of several automated clustering algorithms. One key challenge here is that the populations are not well-approximated by any parametric distribution, and further, the number of populations is not known in advance. Miller and Dunson (2019) cogently demonstrate the substantial inferential advantages of c-Bayes over nonparametric Bayesian models such as mixtures of Gaussian distributions and other alternatives. It is worth pointing out that the manuscript has already garnered close to 100 citations in just two years since its publication. This is a remarkable achievement for an article focusing on statistical theory and methods and is a further testament to the impact and relevance of this research. Several papers primarily authored by biologists and scientists engaged in a variety of data-intensive health-oriented research are also taking note of c-Bayes.

Based upon David’s overall career contributions to the advancement of statistical theory in biometry and, more specifically, this stimulating and highly innovative research manuscript, the conferral of the 2021 George W. Snedecor Award on this outstanding scholar is richly deserved.