Full Name
Rebecca Willett
Job Title
Professor of Statistics and Computer Science & the Faculty Director of AI at the Data Science Institute
Company
University of Chicago
Speaker Bio
Rebecca Willett is a Professor of Statistics and Computer Science & the Faculty 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 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 Executive Committee for the NSF Institute for the Foundations of Data Science. 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.
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.
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.
Speaking At
Abstract
Neural network architectures play a key role in determining which functions are fit to training data and the resulting generalization properties of learned predictors. For instance, imagine training an overparameterized neural network to interpolate a set of training samples using weight decay; the network architecture will influence which interpolating function is learned. In this talk, I will describe new insights into the role of network depth in machine learning using the notion of representation costs – i.e., how much it “costs” for a neural network to represent some function f. Understanding representation costs helps reveal the role of network depth in machine learning. I will show that adding linear layers to a ReLU network yields a representation cost that favors functions with latent low-dimension structure, such as single- and multi-index models.
