Rina Foygel Barber is professor in the department of statistics at The University of Chicago. Previously, she was a National Science Foundation postdoctoral fellow in the department of statistics at Stanford University, supervised by Emmanuel Candès. She earned her PhD in statistics from The University of Chicago in 2012, advised by Mathias Drton and Nathan Srebro, and an MS in mathematics at The University of Chicago in 2009. Prior to graduate school, Barber earned an ScB in mathematics from Brown University and was a mathematics teacher at the Park School of Baltimore from 2005–2007.

The Presidents’ Award parallels the Fields Medal in mathematics and is given annually by COPSS to a young member of the statistical community in recognition of outstanding contributions to the profession. The Presidents’ Award and International Prize in Statistics are the two highest statistics honors.

Barber was recognized for her fundamental contributions to statistical sparsity and selective inference in high-dimensional problems; for the creative and novel knockoff filter to cope with correlated coefficients; for contributions to compressed sensing, the jackknife, and conformal predictive inference; and for the encouragement and training of graduate and undergraduate students.

Huixia Judy Wang, COPSS secretary/treasurer, took a moment to ask Barber several questions, which she answers here.

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

I was stunned! There are so many phenomenal researchers in our field and so many fascinating new ideas and findings each year. It’s an incredible honor to be selected.

Which part of your job do you like the most?

There are so many aspects of my work that I enjoy. I am fortunate to have worked with so many wonderful, talented, and creative students and to have enjoyed collaborations with colleagues around the world who constantly inspire me and who have taught me so much. My favorite moments are when I have the chance to meet with students or collaborators to brainstorm and explore new ideas. I also enjoy teaching and developing new ways to present topics or engage with students in the class. I appreciate that this job is very flexible and the statistics community is very welcoming and family-friendly—with two young children, it’s great to be able to work from home and set my own schedule, and I love that my kids are always welcome on campus, too.

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

They are making a great decision! Statistics is such a dynamic, broad, and fascinating field, and there is such a wide variety of directions to pursue as a career. I would advise new statisticians to search for ideas, problems, and applications that they find fascinating and would love to learn about, even if working in those areas doesn’t seem like it will be immediately productive—investing in broader knowledge and perspectives is always worth it in the long term, in order to pursue deeper questions and to maintain passion for our work.

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

I have been fortunate to have phenomenal mentors throughout my career. In particular, I am so lucky to have worked with my PhD advisers, Mathias Drton and Nathan Srebro, and my postdoc supervisor, Emmanuel Candès. I learned so much from working with each of them and have benefited immensely from their insight and mentorship. They have encouraged me to seek challenges and broader perspectives in my work and have offered invaluable guidance and support. My colleagues in my department have also been an amazing source of support, mentorship, and advice. Finally, I am immensely grateful to my parents and my husband—in addition to their constant support, their own passion for their work in the sciences and the arts has inspired me throughout my career.

Why were you drawn to high-dimensional data, optimization, and multiple testing?

For me, these areas offer the opportunity to study questions that are both mathematically beautiful and extremely practically relevant. I first became interested in these areas after learning about compressed sensing early on in my PhD and have been fascinated by these topics ever since. I love that these topics lead to many surprises, where research in this community takes a sudden turn to discover new ideas about methods that were previously believed to be fully understood, or problems that were previously believed to be impossible. Looking ahead, I’m excited to see how work in these fields continues to integrate with modern large-scale applications and with machine learning tools.

Anything else you would like to share about our profession?

I am incredibly grateful to be part of this community and am constantly inspired by the amazing work of my colleagues. I am also proud to see so many people in our field working toward equality, diversity, and social justice, as well as contributing in countless ways to help support all the efforts of the medical and public health fields during the coronavirus pandemic.

Finally, what are your hobbies/interests beyond statistics?

I love to be outside with my kids and to explore Chicago with them. I also love to read and enjoy knitting and barre fitness.