Ph.D. Degree in Computational and Applied Mathematics

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).

Faculty Openings

The Department of Statistics has openings for two tenure-track positions: an Assistant Professor of statistics and/or data science; an Assistant Professor of computational and applied mathematics.

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Latest News and Announcements

News and Announcements Archive

November 6, 2018Justin Finkel

Meet 2nd Year Student, Justin Finkel.

Justin Finkel, a second year student enrolled in the CAM PhD program, was recently interviewed and profiled by the Physical Sciences Division. Click here: to read the interview and learn more about one of our outstanding students.

October 15, 2018

CCAM Welcomes its New Faculty to the University of Chicago.Daniel Sanz-Alonso and Rebecca Willett

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.

September 25, 2018

Master’s in Computational and Applied Mathematics (MCAM)

The Committee on Computational and Applied Mathematics is excited to announce that we have launched a new Master’s degree program and applications are now being accepted! See the full announcement courtesy of the Physical Science Division here:

For more details about the program, please visit the Master’s in Computational and Applied Mathematics (MCAM) website here: