Maya Sternberg, COPSS Secretary/Treasurer

COPSS award winners (from left): Ryan Tibshirani, Bin Yu, Karen Bandeen-Roche, and Michael Kosorok
Eric Sampson/ASA

The Committee of Presidents of Statistical Societies honored four statisticians at the 2023 Joint Statistical Meetings in Toronto, Canada, on August 9. These awards are jointly sponsored by the COPSS founding partner members: American Statistical Association; Institute of Mathematical Statistics; Eastern and Western regions of the International Biometric Society; and Statistical Society of Canada.

The winner of the 2023 COPSS Distinguished Achievement Award and Lectureship is Bin Yu from the University of California, Berkeley. This award recognizes meritorious achievement and scholarship that has a significant impact on the field of statistical science. The award citation recognized Yu for “fundamental contributions to information theory, statistical and machine learning methodology; for interdisciplinary research in fields such as genomics, neuroscience, remote sensing, and document summarization; and for outstanding dedication to professional service, leadership, and mentoring of students and young scholars.”

The winner of the 2023 Presidents’ Award is Ryan Tibshirani from the University of California, Berkeley. This award is presented annually to a young member of one of the COPSS participating societies in recognition of outstanding contributions to the profession. The award citation recognized Tibshirani for “contributions to nonparametric estimation, high-dimensional inference, and distribution-free inference; for the development of new methodology; for contributions at the interface of statistics and optimization; and for the development of methods for epidemic tracking and forecasting.”

The winner of the 2023 George W. Snedecor award is Michael Kosorok from The University of North Carolina at Chapel Hill. This award is presented biennially (odd years) to recognize an individual who has been instrumental in the development of statistical theory in biometry with a noteworthy publication in biometry within three years of the date of the award. The award citation recognized Kosorok for “foundational, creative, and original contributions to mathematical statistics; for methodological developments in empirical processes and machine learning; for advancement of precision health; and for mentoring of students, postdocs, and junior faculty.”

The winner of the 2023 F.N. David award and Lectureship is Karen Bandeen-Roche from the Johns Hopkins Bloomberg School of Public Health. This award is presented biennially (odd years) to recognize an individual as a role model to others by their contributions to the profession through excellence in research, leadership of multidisciplinary collaborative groups, statistics education, or service to the professional societies. The award citation recognized Bandeen-Roche for “outstanding leadership and service in the biostatistics and statistics community; for her leadership in statistical education; and for her achievements in biostatistical research, particularly in the field of aging research and frailty.”

There were also eight winners of the Emerging Leader Award, formerly known as the COPSS Leadership Academy Award. These awards recognize early-career statistical scientists who show evidence of and potential for leadership to shape and strengthen the future of the statistics field. The 2023 winners are the following:

• Yates Coley
• Lorin Crawford
• Peng Ding
• Edgar Dobriban
• Jingyi Jessica Li
• Avi Feller
• Veronika Rockova
• Gongjun Xu

Looking for more information about the awards? Learn about COPSS award criteria and nominating procedures, read about the Emerging Leader Award winners, or take a look at all the ASA award winners honored at this year’s JSM by viewing the 2023 Awards Book.

A Word with COPSS President’s Award Winner Ryan Tibshirani

 
What was your first reaction to winning the prestigious COPSS President’s Award?
Big surprise! Needless to say, I am very humbled and very grateful to my nominators.

What made you choose to work in the statistics field?
I found my way to statistics due to a mix of being inspired by my dad (who is also a statistics professor) and being drawn to the field by my own interests and inclinations. I studied math and computer science as an undergraduate and became interested in statistics through summer internships in biology labs connected to my dad’s applied collaborations. There, I learned the basics of data analysis ‘on my feet.’

Initially, my interests in statistics were entirely applied. Eventually, I became aware of how broad the field of statistics is and that being a statistician would allow me to pursue applied, methodological, computational, or theoretical questions—any of this is fair game.

Of course, it didn’t hurt that my impressions of statisticians based on those I knew (my dad, Trevor Hastie, Jerry Friedman, and a few others) was that they are an open, curious, and fun crowd. It was then a pretty easy choice to go to graduate school in statistics.

Which part of your job do you like the most?
There is a lot to like. As much as professors might like to grumble on occasion (Who doesn’t?), being a professor is a pretty amazing job. One of the most important aspects to me is intellectual freedom—having the complete freedom to pursue what you want. The way I look at it is the motivation behind doing research is multi-dimensional: one axis measures importance, another measures beauty. Sometimes I pursue things because I find them important and other times because I find them beautiful or interesting. Of course, this is not to say that everything I work on succeeds in being important and/or beautiful. These are just landmarks. The point is that I get the freedom to choose my own approach.

Here are a few other things I like about being a professor:

• Advising students—this can often be a special relationship, and watching your student develop and grow can be really rewarding.
• Teaching—for me, this has actually been one of the best ways to deepen my own understanding and appreciation of various topics and subfields.
• Collaborating—I have been incredibly fortunate with my collaborators so far. Many of my collaborators have become close personal friends, people I would like to continue working and hanging out with for the rest of my life. How lucky I am to have this job?

What advice would you give to young people entering the profession as PhD students or assistant professors at this time?
That is a tough one. There is a lot that comes to mind, but I will just share one idea. I have seen too many people in academia become distressed and unhappy for long periods, including people I saw at one point as persistently positive. A younger version of me would say, “That won’t happen to me,” and carry on as usual, trying not to think too much about it. But my advice is now this: Think carefully about your “value function.” That is, what are you using to measure the value of your work and output (broadly interpreted) to derive a sense of fulfillment and happiness? This can be highly individual, but I believe it is worth thinking about explicitly, and it is never too early to start.

Here are two things that are meaningful to me and have been helpful for me to identify and keep track of: local impact and mutual respect.

The first refers to the impact I have on my students, collaborators, and so on—the people I interact with regularly. I look for evidence that I am contributing positively to these ‘local’ relationships. This makes me happy and is more under my control than, say, where my paper gets published or whether it is cited a lot.

The second refers to the following. I start by identifying the people I really respect. For some subset, I will be lucky enough to be able to develop a relationship with them (e.g., work with them). Over time, I look for signs I may have earned their respect. Such signs can be really special.

Who are your most significant mentors? How did/do they impact your career?
At Stanford (where I was a PhD student): my dad, Trevor Hastie, Jonathan Taylor, and Emmanuel Candes. At Carnegie Mellon (where I spent the first 11 years of my faculty career): Larry Wasserman, Roni Rosenfeld, and Chris Genovese.

I can say a lot about each one and their impact. For Trevor, Jon, Emmanuel, Roni, and Chris, these thoughts will have to be saved for private conversations between us. For my dad and Larry, I will just share a few words.

My dad’s ‘scientific common sense’ is second to none. I’ve always said to myself that if I found myself working on an applied problem of critical importance and I could bring one stats collaborator to the table with me, it would be my dad.

Larry might be the closest person we have in statistics to a ‘universalist.’ The breadth of topics he understands (not superficially, but deeply) truly amazes me.

I have learned so much from each of them, well beyond statistics. They have both accomplished so much and yet are still so kind and generous to everyone around them. Also, they still know how to have fun and remain young at heart.

Why were you drawn to statistical machine learning?
I think there are a few reasons. First, I am genuinely interested in computer science and optimization outside of statistics, so it was natural for me to be drawn to machine learning. Second, ML is a fun, young field full of excitement, and this excitement can be contagious. Third, being at Carnegie Mellon had a big influence on me in this regard. Though I was hired by the department of statistics, the people in the machine learning department were welcoming from day one, and I eventually became jointly appointed in statistics and machine learning. I am actually unsure of whether this would have happened anywhere else. At Carnegie Mellon, statistics and ML are very close and collaborative. At the start of my career, I was interested in ML but didn’t have much experience or credibility as an ML researcher. Still, the ML people welcomed me and helped nurture my interests. It was wonderful and quite formative for me.

What are your hobbies/interests beyond statistics?
Spending time with my family—I have two amazing little kids and a wonderful wife—whether it be at home or on family trips. In terms of current activities, I am mostly biking, swimming, and playing the occasional sport. I played various sports growing up and still like to dabble. I love music and wish I had more time for it. I wish I had more time for reading books, too. Maybe I will make more time for music and books in the future.