The winners of the Committee of Presidents of Statistical Societies (COPSS) Awards in 2024 were announced in the April/May issue. This year, COPSS awarded the E. L. Scott Award and Lectureship to Regina Liu, the Distinguished Achievement Award and Lectureship to Robert Tibshirani, and the Presidents’ Award to Veronika Rockova. Read more about the winners below.

Regina Liu: E.L. Scott Award and Lectureship

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.

Annie Qu writes: Regina Liu is selected to receive the 2024 Elizabeth L. Scott Award based on her outstanding achievements in research, education, service and leadership, and her superlative contributions in fostering opportunities for women, underrepresented groups and junior researchers in statistics and nurturing their careers.

Regina is a Distinguished Professor in the Department of Statistics at Rutgers University. She has been a part of the Rutgers community since 1983, including serving as department chair from 2005–2020.

Regina is a leading and world-renowned researcher in data depth and resampling. She introduced her ‘simplicial depth’ (in Annals of Statistics, 1990) which laid the foundation and framework for the notion of data depth. Her discussion paper in Annals of Statistics (1999) developed data depth into a systematic multivariate statistical approach, as a viable nonparametric alternative to the traditional normality-based approach. In addition, on resampling, Regina was among the first to make significant contributions to bootstrap outside the realm of i.i.d. settings. For example, her paper with K. Singh was the first to propose bootstrap for correlated data and coin the now standard terminology, moving block bootstrap.

Regina has an exceptional service record to Rutgers and the statistics profession. She has served on numerous editorial boards, and professional and university committees. In her service roles, she has always made sure to emphasize the importance of women’s participation. A case in point is that when she served as a co-editor for the Journal of American Association Statistics, she made sure that the editorial board would have significant female and underrepresented members. She has also subsequently recommended and supported several female associate editors to become chief editors of journals.

Over the years, Regina has been appointed to many leadership roles. She served as the department chair at Rutgers for 15 years. Her vision and selfless efforts have been widely credited for the significant rise in standing of our department. She has been a tireless advocate and mentor for our junior faculty members. In addition, Regina is a great leader who put in many long hours and great effort and thought for her role as the IMS president. Among her many initiatives and activities, diversity, equity and inclusion were among her top priorities. She has systematically recruited females and underrepresented group to make sure that they are well represented in IMS committees or editorship positions. She also worked closely with the DEI committee to form new initiatives to broaden membership base among females.

Regina is well known to be a dedicated, inspiring instructor and mentor for her students. She has been a role model for many students and junior researchers, especially female researchers. Many students, and her colleagues in the department of Rutgers, benefit from her kindness, generosity, wisdom, and high moral standard.

In summary, Regina is an outstanding, tireless, selfless and courageous leader who has served as a role model and inspiration for many female statisticians. She exemplifies the true spirit of Elizabeth L. Scott’s lifelong efforts to advance the careers of women in academia.

Robert Tibshirani: Distinguished Achievement Award and Lectureship

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.

Daniela Witten and Limin Peng write: The Committee of Presidents of Statistical Societies selected Robert Tibshirani, Professor of Biomedical Data Science and Professor of Statistics at Stanford University, for the 2024 Distinguished Achievement Award and Lectureship, which recognizes researchers who have made exceptional contributions to statistical methods with significant impact on scientific investigations. Tibshirani’s lecture at the 2024 Joint Statistical Meetings was titled “Pre-training and the Lasso”.

Tibshirani has played a key role in many of the most important statistical developments of the past 40 years, including generalized additive modeling, false discovery rate estimation, the lasso and related methods for high-dimensional modeling, and post-selection inference. Tibshirani’s early research on generalized additive models dramatically extended the flexibility of traditional linear regression, and generalized additive models are now standard techniques for nonparametric multiple regression. Tibshirani’s work on the Least Absolute Shrinkage and Selection Operator (lasso) was a major breakthrough in statistical methodology and theory that has transformed the practice of feature selection and high-dimensional modeling in biomedicine and other scientific fields. The original paper on the lasso has been cited over 55,000 times. Tibshirani’s foundational contributions to the field of machine learning have bridged the gap between the algorithmic-type thinking that is pervasive in the field, and “classical” statistical thinking. Tibshirani has also had a substantial impact on the field of genomics, through his pioneering work tackling the statistical challenges associated with high-throughput gene expression data, and by popularizing ideas in multiple testing and false discovery rate estimation for a wide biological audience. More recently, Tibshirani and his collaborators played a key role in developing the area of post-selection inference for penalized regression models; this work has contributed to moving the field of statistical machine learning from its original focus on prediction to its more recent focus on uncertainty quantification.

Tibshirani’s work has received almost 500,000 citations, and he has an h-index of 181. In addition to hundreds of published articles, Tibshirani has co-authored five bestselling textbooks on topics ranging from the bootstrap to generalized additive models to statistical machine learning. His talent of distilling a complicated idea into its most simple and accessible essence shines through in his textbooks. His co-authored textbook, Elements of Statistical Learning (with T. Hastie and J. Friedman), is considered by many to be the “Bible of machine learning” and remains a key reference for more than 20 years since its publication.

As noted by his PhD advisor Brad Efron, “Rob is arguably the most influential applied statistician working today”. In the words of his PhD student Larry Wasserman, “Few statisticians have made a single contribution that has had a lasting impact on either the field of statistics or on fields that use statistical methods … Rob has made a number of such contributions.”

In addition to his statistical brilliance, Tibshirani is known for his infectious enthusiasm, and for the mentoring that he has provided for generations of trainees.

Tibshirani studied at the University of Waterloo (BS in Mathematics and Statistics in 1979), University of Toronto (MS in Statistics in 1980), and Stanford University (PhD in Statistics in 1984). He then joined the faculty of University of Toronto in 1985, where he remained until a move to Stanford University in 1998.

Among his many awards, Tibshirani has won the Guggenheim Foundation Fellowship (1994), the COPSS Presidents’ Award (1996), the Gold Medal from the Statistical Society of Canada (2012), and the International Statistical Institute Founders of Statistics Prize (2021). He is also a fellow of the Royal Society of Canada (2001), a fellow of the Royal Society of the UK (2019), and a member of the US National Academy of Sciences (2012).

Robert Tibshirani’s fundamental contributions to methods, theory, and applications of statistics and machine learning make him a highly-deserving recipient of the COPSS Distinguished Achievement Award and Lectureship.

Veronika Rockova: COPSS Presidents’ Award

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 Secretary/Treasurer Maya Sternberg interviewed Veronika Rockova to find out more about her career and life:

What was your first reaction to winning the prestigious COPSS Presidents’ award?

I was at a black-tie dinner event when I received a phone call from an unknown number. I did not pick up, thinking it was spam. I then received a text from Bo Li saying she had some important news for me. It was not until much later in the evening when it dawned on me that it might be COPSS. But this would be the last thing I expected. The next day, Bo called me again and I cried on the phone. How could it be me?

Which part of your job do you like the most?

The eureka moment of discovery. I like the confirmation of my intuition when research ideas work out the way I had anticipated. The best part is sharing such moments with my collaborators and students. Working alone is not nearly as much fun. I enjoy exchanging ideas and mentoring. The progress and gratitude of my students/postdocs is the biggest reward.

What advice would you give to young people who are entering the profession as PhD students and assistant professors at this time?

I would advise them not to follow any advice. Just be yourselves and develop your own personal brand and style. Do not try to follow in someone’s research footsteps. Create your own path building on your own ideas despite what others may think.

Who are your most significant mentors? How did/do they impact your career?

My postdoc adviser Edward George has been my academic father. I do not think I would have turned out to be a professional academic if it had not been for him. He believed in me when others did not, including myself. He gave me a postdoc opportunity and provided me with a very encouraging environment. His mentorship included navigating tough academic waters as well as tenure pressure. I am trying to pay it forward with my advisees now. It is so important to be encouraging and to bring out the best in people. Ed taught me that. Besides Ed, my former PhD adviser Emmanuel Lesaffre who gave me my thesis topic “Bayesian Variable Selection”. He triggered my interest in Bayes, advised my master’s thesis in Belgium and offered me a PhD position.

Why were you drawn to Bayesian statistics?

I took my first Bayesian class at Charles University in Prague during my master’s degree in mathematical statistics. That class was taught more as an obligation rather than as an integral component of the statistics curriculum. It was not until my Bayesian course at Universiteit Hasselt in Belgium (taught by Emmanuel Lesaffre) when I started appreciating the intuitive appeal of Bayes. It always felt to me like the most natural way of thinking. I lean towards things I understand more effortlessly. Bayes is still under-represented and I am particularly proud of being one of the few Bayesians to have won the COPSS Presidents’ Award. Go Bayes!

How would you describe your Statistics journey?

I love statistics. However, my path forward has not always been easy. Initially, I had to face challenges being one of the very few women in a mathematics (statistics) program at Charles University in Prague. I always felt like people, for some reason, did not expect much of me. I remember our mathematical analysis professor in the first semester starting off his first lecture with the statement: “At the end of the semester, most of you won’t be here, especially women.” I internalized this comment by assuming that I was not supposed to be there to begin with. This feeling has never truly gone away. Before I left for my second master’s degree at Universiteit Hasselt (Belgium), one of the professors in Prague told me “We hope we won’t have to be ashamed of you” even though I was a straight A student. I may have looked for an escape and found the field of biostatistics as a viable alternative to mathematics. At Universiteit Hasselt and later during my PhD in biostatistics at Erasmus University in Rotterdam (the Netherlands), I felt like I had to overcome social integration challenges because I was born in what people still call “Eastern Europe”. Once again, I felt like people were making some assumptions which I had to try hard to dissolve. Moving to the USA was yet another challenge. I became a US citizen only a few months ago, but my social assimilation is still ongoing. I think the field of Statistics (and Econometrics) could be a bit less tribal and more open-minded. We are all trying to advance knowledge in the best way we can. Happily, the Bayesian community quickly adopted me. I felt like I was accepted since the beginning. I do not think I can easily say that about many other academic environments I have been in. I like to think that regardless of who you are and where you come from, talent is universal. While Statistics has a more supportive culture compared to other disciplines (such as Economics or Econometrics), I think that there is still an unnecessary elitism with cellophane barriers to entry.

Finally, what are your hobbies/interests beyond statistics?

I have been playing classical piano since the age of six. I still take lessons. Music is my creative outlet and a subtle mode of communication. I like that, unlike Statistics, playing music involves not only good memory and brain acuity, but also emotional connection and expression. I feel more human when I play the piano. I am also an avid tennis player and a golf neophyte.

A Little More about Veronika Rockova

Veronika Rockova was born in a small town in Czechoslovakia between Prague and Vienna. Growing up in a village of 1600 people, she attended a small high school (just two classes of 24 children each year) from which a prominent academic career would seem most unlikely. In spite of the odds, she was admitted to the top Czech program in mathematics at Charles University in Prague, where she pursued a degree in general mathematics. After several introductory probability and statistics classes, she realized that she was predestined for the “mathematics of chance”. A bachelor’s degree in mathematics there served as a springboard for her further studies: a Master’s degree in mathematical statistics at Charles University and a Master’s degree in biostatistics at Universiteit Hasselt. Collaborating with the department of hematology on AML research during her PhD in biostatistics at Erasmus Medical Centre in Rotterdam, she developed an interest in applied statistics, which led her to the main thrust of her doctoral thesis, methodological research in the area of Bayesian variable selection. During her postdoctoral training at the Wharton School of the University of Pennsylvania, she deepened her interest in the development of new Bayesian methodology for dimension reduction (including factor analysis and sparse linear models).

After joining the Booth School of Business at the University of Chicago as an assistant professor in 2016, she broadened her research agenda to include theoretical aspects of popular Bayesian machine learning methods (such as Bayesian CART and BART). Leveraging machine learning for Bayesian computation has become a major theme in her more recent work. She was recognized by the NSF CAREER Award in 2020, the year she was promoted to associate professor. In 2022, she became a full professor and was recognized again in 2023 with the COPSS Emerging Leader Award.

In her work, Veronika continues to champion Bayes on various fronts: (1) tackling computational challenges by developing new faster algorithms, (2) providing much needed theoretical justifications, (3) creating new methodology using the most current machine learning constructs.

She currently serves on the editorial boards of the Annals of Statistics, Journal of the American Statistical Association and Journal of the Royal Statistical Society (Series B).

At the University of Chicago, she participates in several initiatives that promote the role of Statistics in society (Centre of Applied AI at Booth, Committee on Quantitative Methods in Social, Behavioral and Health Sciences, Data Science Institute). She is also an affiliate faculty at the Department of Statistics at the University of Chicago.