CCAM Welcomes its New Faculty to the University of Chicago

October 15, 2018

Dr. Daniel Sanz-Alonso is a new assistant professor in the Department of Statistics, and Dr. Rebecca Willett is a new professor in the departments of statistics and computer science. Both will be developing new courses for the CAM PhD program, and expect to advise CAM PhD students.

Professor Sanz-Alonso’s academic interests include data assimilation, inverse problems, machine learning, Monte Carlo methods, and uncertainty quantification. His research has been published in the Society for Industrial and Applied Mathematics (SIAM)/American Statistical Association (ASA) Journal on Uncertainty Quantification, the SIAM Journal on Mathematical Analysis, Communications in Mathematical Sciences, Inverse Problems, Statistical Science, and Physica D: Nonlinear Phenomena. Daniel completed a licenciatura degree in mathematics from the University of Valladolid in Spain, followed by a PhD in mathematics and statistics from the University of Warwick in the United Kingdom. Most recently, Prof. Sanz-Alonso was a postdoctoral research associate in the Division of Applied Mathematics at Brown University, and a member of its Data Science Initiative.

Professor Rebecca Willett joins the University of Chicago faculty from the University of Wisconsin-Madison, where 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. Her research interests include signal processing, machine learning, and large-scale data science. In particular, she has studied methods to leverage low-dimensional models in a variety of contexts, including when data are high-dimensional, contain missing entries, are subject to constrained sensing or communication resources, correspond to point processes, or arise in ill-conditioned inverse problems. This work lies at the intersection of high-dimensional statistics, inverse problems in imaging and network science (including compressed sensing), learning theory, algebraic geometry, optical engineering, nonlinear approximation theory, statistical signal processing, and optimization theory. Becca's group has made contributions both in the mathematical foundations of signal processing and machine learning and in their application to a variety of real-world problems. She has active collaborations with researchers in astronomy, materials science, microscopy, electronic health record analysis, cognitive neuroscience, precision agriculture, biochemistry, and atmospheric science.

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