An increasing interest in analyzing and modeling high-dimensional data, both static and dynamic, and handling very large data sets in multiple scientific domains has led to an increased flow of ideas between applied mathematics, statistics, and computation. A synergy is emerging among fields such as statistical modeling of high-dimensional data, parameter estimation, optimization, machine learning, dynamical systems, numerical methods, and computational mathematics. Examples can be found in the exciting recent progress in the genome sciences, where statistically based computation has become an intrinsic part of research in human genetics; in the growing interaction between spatial statistics and the numerical analysis of atmospheric and ocean dynamics data, driven in large part by extensive new data sets being acquired through satellite imaging; in the extensive use of statistical modeling and probabilistic analysis for data analysis and modeling in neuroscience. Another aspect of these developments is the rapid convergence of research interests between Statistics and Computer Science. The traditional computer sciences domain of Artificial Intelligence has, during the past decade, increasingly adopted statistical methods, to the point that, today, the two fields are entirely integrated.

The history of applied mathematics at the University of Chicago is filled with great names who have had major impact in analysis, PDE's, harmonic analysis, numerical methods, and statistics. Moreover, in the past decade the University has created the Computation Institute, which serves as a focal point for projects involving high-end and massively parallel computing. And yet we have not seen emerge a consistent program in computational and applied mathematics that is able to meet the challenges presented by these new developments, both in terms of research and in terms of graduate and undergraduate education. Given the strong interdisciplinary ties developed by faculty in the Department of Statistics with quite a number of scientific fields, and the ongoing emphasis on integrating theory and methodology with real problems and data, it seemed a natural home for developing such a program, which was formally initiated in 2008.

In the past four years we have hired a number of faculty with background and interests in a variety of domains involving computational and applied mathematics. Nina Hinrichs works on modeling protein dynamics and was hired jointly with Computer Science; Lek-Heng Lim is an applied mathematician studying numerical methods involving tensors and their application to large and high-dimensional data sets; John Reinitz is a mathematical biologist hired jointly with Ecology and Evolution; John Lafferty and Risi Kondor both work in machine learning and the analysis of high-dimensional data and were hired jointly with Computer Science; Nicolas Brunel is a computational neuroscientist hired jointly with Neurobiology; and Mihai Anitescu works in numerical methods and optimization and is a joint hire with Argonne National Laboratory.

These exciting new hires come in addition to many existing faculty in the Departments of Statistics, Mathematics, Computer Science, Physics, Astrophysics, Chemistry, Geophysical Science, who are involved in various aspects of applied and computational mathematics. Our hope is that this hiring initiative will create a core group that will work, jointly with the numerous faculty across campus, to create a strong integrated educational and research program in computational and applied mathematics. As first steps in this direction, we have developed a year-long graduate course sequence in computational mathematics, covering matrix computation, optimization, and numerical methods for differential equations, and we have developed an informal sequence in optimization jointly with faculty in the Business School and at Toyota Technological Institute. Furthermore, we are developing two new graduate sequences—one in computational neuroscience and the other in machine learning. Finally, we have also initiated a new seminar series, "Statistical and Scientific Computing," that has drawn a growing audience from across the physical and biological sciences.