About the series
Investing in applied machine learning without understanding the underlying foundations is like investing in healthcare without understanding biology. Foundational research has had a tremendous impact on machine learning, from optimization methodology to privacy protections and from data acquisition strategies to uncertainty quantification. Understanding machine learning foundations has also deepened our insights into longstanding challenges in statistics, optimization theory, numerical simulations, and engineering. We have yet to witness the full impact of these foundational developments. In this talk, I will highlight several major examples of these foundations and their impacts on theory, practice, and workforce development. Furthermore, we will explore emerging directions in machine learning for which new theory is necessary and where future foundational research is likely to play a critical role, including scientific discovery and ensuring machine learning methods are fair, safe, equitable, and sustainable.
Rebecca Willett is a Professor of Statistics and Computer Science and the Director of AI in the Data Science Institute at the University of Chicago, and she holds a courtesy appointment at the Toyota Technological Institute at Chicago. Her research is focused on machine learning foundations, scientific machine learning, and signal processing. 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 NSF Institute for the Foundations of Data Science Executive Committee. She 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. 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 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.
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