Rebecca Willett is a Professor of Statistics and Computer Science & Director of AI at the Data Science Institute, with a courtesy appointment at the Toyota Technological Institute at Chicago. Her research is focused on machine learning, signal processing, and large-scale data science. Willett received the National Science Foundation CAREER Award in 2007, was a member of the DARPA Computer Science Study Group, received an Air Force Office of Scientific Research Young Investigator Program award in 2010, was named a Fellow of the Society of Industrial and Applied Mathematics in 2021, and was named a Fellow of the IEEE in 2022. She is the Deputy Director for Research at the NSF-Simons Foundation National Institute for Theory and Mathematics in Biology and a member of the Executive Committee for the NSF Institute for the Foundations of Data Science. She is Faculty Director of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship and helps direct the Air Force Research Lab University Center of Excellence on Machine Learning. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018.

Prof. Willett’s work in machine learning and signal processing reflects broad and interdisciplinary expertise and perspectives. She is known internationally for her contributions to the mathematical foundations of machine learning, large-scale data science, and computational imaging. Her research focuses on developing the mathematical and statistical foundations of machine learning and scientific machine learning methodology. In addition to her technical contributions, Prof. Willett is a strong advocate for diversity in STEM and AI and has organized multiple events to support women in middle school, as undergraduate and graduate students, and as faculty members.

Leadership roles:

• Deputy Director for Research at the NSF-Simons Foundation National Institute for Theory and Mathematics in Biology
• Member of the Executive Committee for the NSF Institute for the Foundations of Data Science.
• Faculty Director of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship
• Deputy Director of the Air Force Research Lab University Center of Excellence on Machine Learning.

Advisory board roles:

• National Science Foundation’s Institute for Mathematical and Statistical Innovation, Scientific Advisory Board
• National Academies of Sciences, Engineering, and Medicine Committee on Testing, Evaluating, and Assessing Artificial Intelligence-Enabled Systems under Operational Conditions for the Department of the Air Force
• National Academies of Sciences, Engineering, and Medicine Committee on Foundational Research Gaps and Future Directions for Digital Twins 
• US Department of Energy Office of Science Advanced Scientific Computing Advisory Committee (ASCAC) Sub-committee on “AI for Science”
• Sandia National Laboratories Computing and Information Sciences Laboratory, External Advisory Board
• University of Tokyo Institute for AI and Beyond, International Advisory Board
• National Science Foundation’s Institute for the Foundations of Machine Learning, External Advisory Board Member
• Chan Zuckerberg Initiative, Scientific Advisory Board on Imaging

Scientific community leadership roles:

• Inaugural role in SIAM Journal of Mathematics of Data Science
• Inaugural role in Harvard Data Science Review
• Broad experience with leading conferences and workshops for the AI community
• Extensive experience in broadening participation in AI across diverse communities, including through Graduate Research Opportunities for Women (GROW)
• Conference chair or conference technical program chair for multiple large-scale conferences and workshops focused on AI

Additional activities:

• Elected Vice Chair of the SIAM Activity Group on Imaging Science
• Plenary speaker at SIAM Conference on Uncertainty Quantification (UQ22)
• Kirk Lecture at the Isaac Newton Institute
• Speaker at National Academies Symposium on Mathematical Challenges for Machine Learning and Artificial Intelligence
• Panelist at AI for Science: From Atoms to the Cosmos | Argonne National Laboratory
• Speaker at DeepMath2021
• Cray Distinguished Speaker, 2021
• SIAM Fellow, Class of 2021
• Co-PI of the Institute for Foundations of Data Science (IFDS), a four-university collaboration among the Universities of Washington, Wisconsin-Madison, California Santa Cruz, and Chicago, supported by the NSF Transdisciplinary Research In Principles Of Data Science (TRIPODS) program
• Member of International Advisory Board for the Institute of AI and Beyond at the University of Tokyo (press release)
• Featured Speaker at Innovation X Lab Artificial Intelligence Summit, 2019
  
• Congratulations to Dr. Eric Hall, winner of the 2018 IEEE SPS Young Author Best Paper Award for “Online Convex Optimization in Dynamic Environments”, IEEE Journal of Selected Topics in Signal Processing, Volume 9, Number 4, June 2015
• Keynote speaker at Biological and Astronomical Signal Processing (BASP) Frontiers workshop, 2019
• Technical Co-Chair for ICIP 2022 in Bordeaux, France
• Technical Co-Chair for IEEE Data Science Workshop, Minneapolis, June 2-5, 2019
• Technical Co-Chair for SampTA 2019
• Member of Organizing Committee for SIAM CSE19
• Invited talk at Institut Henri Poincaré workshop on variational methods and optimization in imaging
• Co-PI on new Air Force Center of Excellence on Efficient and Robust Machine Learning
• Recipient of the 2018 Gerald Holdridge Teaching Excellence Award
• 5-minute general audience talk at Wisconsin Science Festival
• Interviewed on Wisconsin Public Radio
• Speaker at Wisconsin Science Festival 
• Co-PI of NSF Institute for Foundations of Data Science (TRIPODS)
• Invited speaker at 3rd International Matheon Conference on Compressed Sensing and its Applications
• Invited speaker at ACNTW Workshop on Optimization and Machine Learning
• Invited speaker at Big Data and Ecoinformatics in Agricultural Research; video here
• Invited speaker at SIAM Annual Meeting 2017
• Invited speaker at learning theory workshop at FoCM, 2017
• Invited speaker at 61st World Statistics Congress  — ISI2017
• Plenary speaker at SPARS 2017