The University of Chicago has recently created a Committee on Computational and Applied Mathematics (CCAM), an inter-departmental program to provide graduate training leading to the Ph.D. in Computational and Applied Mathematics. Please note our application for Fall 2018 is now closed as the deadline of January 15, 2018 has passed.
The use of computational, mathematical and statistical modeling in various areas of science has increased dramatically in recent years, triggered by massive increases in computing power and data acquisition. Mechanistic models for physical problems that reflect underlying physical laws are being combined with data-driven approaches in which statistical inference and optimization play key roles. These developments are transforming research agendas throughout statistics and applied mathematics, and are impacting a broad range of scientific disciplines.
A critical need now exists to train the next generation of computational and applied mathematicians to confront data-centric problems in the natural and social sciences. In response to these developments, the Committee on Computational and Applied Mathematics has been formed to provide graduate training in Computational and Applied Mathematics that reflects both the scientific demands and the unique strengths of the University of Chicago faculty across the Division of the Physical Sciences, including the recent hiring of several new faculty under a Computational and Applied Mathematics Initiative (CAMI).
The Department of Statistics has an opening for a Senior Lecturer who will work closely with CCAM. The Department also has openings for two tenure-track positions: an Assistant Professor of statistics and/or data science; an Assistant Professor of computational and applied mathematics.
The University of Chicago is an Affirmative Action/Equal Opportunity/Disabled/Veterans Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national or ethnic origin, age, status as an individual with a disability, protected veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination at http://www.uchicago.edu/about/non_discrimination_statement/. Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-0287 or email ACOppAdministrator@uchicago.edu with their request.
Justin Finkel, a second year student enrolled in the CAM PhD program, was recently interviewed and profiled by the Physical Sciences Division. Click here: https://physical-sciences.uchicago.edu/page/justin-finkel to read the interview and learn more about one of our outstanding students.
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.