Rebecca Willett Receives the SIAM Activity Group on Data Science Career Prize
Dr. Rebecca Willett is the 2024 recipient of the SIAM Activity Group on Data Science Career Prize. This is the first time that the prize will be awarded.
She received the prize for “her work in physics-informed machine learning and data science and for her service and leadership in the data science community.” From her pioneering work on photon-limited imaging to her analysis of generalization in overparameterized neural networks, Dr. Willett’s work encompasses both the mathematical and statistical foundations of data science and the structure and context of problems from the natural sciences. Her research in physics-informed machine learning has contributed to bridging the gap between theoretical research and practical applications.
The SIAM Activity Group on Data Science awards this prize every two years to an outstanding senior researcher who has made broad and influential contributions to the mathematical, statistical or computational foundations of data science. The prize recognizes a research career in the mathematics of data science at the highest level of achievement.
Dr. Willett is a professor of statistics and computer science and the Director of AI in the Data Science Institute at the University of Chicago. She also holds a courtesy appointment at the Toyota Technological Institute at Chicago. She completed her Ph.D. in electrical and computer engineering at Rice University (2005) and was an assistant professor, then tenured associate professor of electrical and computer engineering at Duke University (2005-2013). From 2013 to 2018, Dr. Willett was an associate professor of electrical and computer engineering, a Harvey D. Spangler Faculty Scholar, and a fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison. Her research focuses on machine learning foundations, scientific machine learning, and signal processing.
Dr. Willett has been a SIAM member for 11 years and was named a SIAM Fellow in 2021, as well as an IEEE Fellow in 2022. She currently serves as the Deputy Director for Research at the NSF-Simons Foundation National Institute for Theory and Mathematics in Biology and is a member of the NSF Institute for the Foundations of Data Science Executive Committee. Additionally, Dr. Willett is the 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. Early in her career, she received the National Science Foundation CAREER Award (2007) and the Air Force Office of Scientific Research Young Investigator Program award (2010). She was also a member of the DARPA Computer Science Study Group. Learn more about Dr. Willett.
Q: Why are you excited to receive the award?
A: Receiving the inaugural SIAM Activity Group on Data Science Career Prize is a profound honor. My most significant research endeavors have always been a result of collaboration with other scholars— from seasoned researchers who guided and mentored me, to trainees who challenged my perspectives, and from fellow experts in my field to dedicated scientists and engineers from diverse disciplines. I have been fortunate to belong to a scholarly community that values collegiality alongside rigor and impact.
Q: Could you tell us about the research that won you the award?
A: My research focuses on the foundations of machine learning, scientific machine learning, and signal processing. In my foundational work, I develop new optimization methods, statistical inference tools, and mathematical analyses of neural networks and other high-dimensional data-fitting models. These ideas aim to enhance the rigorous use of machine learning and signal processing across a wide range of application domains. My work on scientific machine learning addresses challenges related to designing sequences of experiments, integrating physical models and constraints into machine learning, accelerating numerical simulations and solvers, and developing new quantitative models of physical phenomena from data. These topics are relevant across the natural sciences and engineering and are crucial for the principled and rigorous use of machine learning in scientific research.
Q: What does your work mean to the public?
A: Data science, particularly machine learning, has become an integral part of our daily lives. It influences areas such as healthcare, criminal justice, and personal finance. Understanding the robustness, accuracy, and potential failure modes of data science methods is crucial in these contexts. My research on the mathematical foundations of data science, machine learning, and signal processing contributes to the theories and tools that facilitate this understanding, enhancing the reliability and applicability of these methods in real-world scenarios.
Q: What does being a member of SIAM mean to you?
A: SIAM research has a significant impact across various disciplines and domains due to the community’s emphasis on high-quality, rigorous mathematics. This commitment is evident in all their activities, particularly in SIAM conferences. These conferences are specifically designed to provide scholars, including trainees, with valuable feedback on their work and to help them identify opportunities for new collaborations and research directions. Every SIAM conference I attend leaves me feeling reinvigorated, energized, and inspired. The inclusive and supportive culture of SIAM conferences significantly contributes to the high quality of papers published in SIAM journals.
This article was pulled and formatted from the siam.org website.
