Daniela Witten wins the prestigious 2022 COPSS Presidents’ Award

Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data. Much of her work is motivated by applications in genomics and neuroscience. Daniela is passionate about communicating key ideas from statistical machine learning to a broad audience, and is co-author of the very popular textbook Introduction to Statistical Learning. Daniela completed a BS in Math and Biology with Honors and Distinction at Stanford University in 2005, and a PhD in Statistics at Stanford University in 2010.

The Presidents’ award citation states that Daniela Witten was selected, “For bridging the gap between the questions that scientists are asking about their data and the statistical methods that are available to provide insightful answers, especially in the context of biomedical research; for developing flexible and interpretable approaches for modeling large-scale and high-dimensional data; and for the significant elevation of statistical science via successful translation of statistical ideas to a broad audience.”

What follows is Daniela’s interview with Amita Manatunga, COPSS Chair, and Maya Sternberg, COPSS Secretary/Treasurer.


Q1. What was your first reaction to winning the prestigious COPSS President’s Award?

Oh gosh. I got an e-mail from Tianxi Cai (chair of the award committee) in late January notifying me of the award, and honestly, my first reaction was that she had sent it to the wrong email address. My second reaction was to double-check that the date was not April 1. My third reaction was to text my husband.

After things had settled a bit, I had the opportunity to reflect on the community that has made not just this award, but my entire career, possible. These include my wonderfully supportive colleagues at University of Washington Departments of Statistics and Biostatistics, my collaborators near and far, the absolutely fantastic PhD students that I’ve had over the past 12 years, and my family. Also Rob Tibshirani, my PhD advisor—more on that later.

Q2. Which part of your job do you like the most?

Hands down, my favorite part of the job is working with grad students. I have had the privilege of working with immensely talented PhD students during the past 12 years. Nothing beats a front row seat to the development of a first-year student into an independent researcher. I take immense pride in the accomplishments of all of my students, and in the ways (both large and small) that I’ve been able to help them along their academic journeys.

And I love the intellectual freedom that my job provides. I can spend all day (or at least, part of the day… on some days… a couple of days per month when time allows…) learning about things that interest me. What could be better?!

Q3. What advice would you give to young people who are entering the profession as PhD students and assistant professors?

Getting a PhD, and working towards tenure, is not easy. Actually, it’s extremely hard: the academic system never misses an opportunity to remind us of all the things that we don’t know, and all of the ways in which our work is not “good enough.” So, acknowledge that what you’re doing is hard, and that you don’t need to be perfect, and that a lot of people are finding it just as hard as you are. (I certainly did at your career stage, and sometimes still do!)

I wrote up my top tips for PhD students in a recent installment of “Written by Witten,” my column for the IMS Bulletin: https://imstat.org/2022/04/01/written-by-witten-so-long-and-thanks-for-all-the-tips/

And I’ll sum it all up with a quote from Dory from the movie Finding Nemo: “Just keep swimming.” 

Q4. Who are your most significant mentors? How did/do they impact your career?

My most significant mentor has been my PhD advisor, Rob Tibshirani. He has provided unflagging support every step of the way, starting from the beginning of my PhD and continuing to this day. I’ve learned so much from him, not just about the field of statistics, but also about how to interact with the world as a scientist and a person. He’s also set an incredibly high bar in terms of how to be a PhD advisor, which I will spend the rest of my career trying to meet with my own students. (I’ll stop now so I don’t embarrass him.)

I’ve also learned that mentorship doesn’t need to be a one-stop shop: I’ve relied on different people for different types of mentorship throughout my career. Gareth James, my co-author on Introduction to Statistical Learning, has taught me the value of patience (actually, he’s still teaching me this). Ali Shojaie’s time as junior faculty at UW overlapped with mine, and he helped me through so many hurdles during that period. Jacob Bien, a long-time friend and collaborator, has reminded me that it’s very important for research to be fun. I’ve learned a lot from some of my senior colleagues at UW, especially former department chairs Bruce Weir and Patrick Heagerty. 

I’m fortunate to have been able to surround myself with a supportive community of women. This includes Ya Xu, Sarah Emerson and Layla Parast from my grad student days, and Emily Fox and Amy Willis from my time as faculty. It also includes senior women who have provided ongoing inspiration and a listening ear over the years: Liza Levina, Francesca Dominici, Bhramar Mukherjee, Florentina Bunea, among others. 

Q5: Why were you drawn to statistical machine learning?

During my first year of grad school at Stanford, I took the two-quarter PhD sequence in statistical machine learning, taught by Trevor Hastie and Jerry Friedman out of the textbook Elements of Statistical Learning. I fell for the field—hook, line, and sinker! I was fascinated by the idea that so many of the seemingly mysterious ideas and algorithms in machine learning and artificial intelligence could be demystified—and improved upon!—through a solid understanding of statistics. And I was intrigued by the possibility of developing and applying these types of methods to solve problems in biology.

Q6. Anything else you would like to share about our profession?

I feel so thankful to have found a career that has allowed me to spend my time learning new things and working with talented and kind people. I have felt welcomed into this field ever since I started grad school in 2005.

However, I recognize that my experience has been shaped in large part by my immense privilege. To name just a few aspects of this privilege: I am white, American, and a native English speaker; I did my undergrad at Stanford; and my parents are academics.

I hope that we can commit, as a field, to ensuring that our profession is a welcoming place for all, and especially for members of groups that have been historically marginalized in academia and in the mathematical sciences. And I’d like to express my immense gratitude to the many statisticians who are already working towards this goal every single day.

Q7: Finally, what are your hobbies/interests beyond statistics?

Outside of work, I spend most of my time with my husband and three children, who are aged 3, 6, and 8. I love to cook, eat, travel, and spend time with friends. I also try to squeeze in some exercise (I am an enthusiastic runner, and you can learn more about my relationship with my Peloton bike in my first “Written by Witten” column in the IMS Bulletin https://imstat.org/2021/09/30/written-by-witten-reflections-on-19-months-of-work-from-home/).