Analyzing and modeling high-dimensional data and handling very large data sets has led to an increased synergy between applied mathematics, statistics, and computation. The history of applied mathematics at the University of Chicago is filled with great names who have made major contributions, and the University already houses the Computation Institute. Given the strong long-standing interdisciplinary ties the Department of Statistics has always had with other fields of study, it is the natural home for the Computational and Applied Mathematics Initiative (CAMI), which started formally in 2008 and has been growing rapidly ever since. Exciting new faculty hires, engaging seminar series, attracting the best students eager to gain the tools to be the leaders of tomorrow and the innovators of new applications—this is what CAMI is about and what our Department is dedicated to.
We’re glad your interest has drawn you to our website. Explore the CAMI section here and get a taste of what we’re passionate about in Statistics at the University of Chicago.
- Ph.D. Degree in Computational and Applied Mathematics
- Master's Program in Computational and Applied Mathematics
- Computational and Applied Mathematics Colloquium (This colloquium supersedes the Scientific and Statistical Computing Seminar that met from Fall 2011–Spring 2017.)
CAMI Faculty
- Mihai Anitescu (Statistics, Argonne National Laboratory)—numerical analysis and optimization
- Guillaume Bal (Statistics, Mathematics)—applied mathematics, partial differential equations with random coefficients, theory of inverse problems
- Risi Kondor (Computer Science, Statistics)—machine learning
- Lek-Heng Lim (Statistics)—numerical analysis and optimization
- John Reinitz (Statistics, Ecology and Evolution, Molecular Genetics and Cell Biology)—computational biology
- Daniel Sanz-Alonso (Statistics)—data assimilation, inverse problems and machine learning
- Mary Silber (Statistics)—applied dynamical systems
- Rebecca Willett (Statistics, Computer Science)—computational neuroscience, environmental and spatial statistics, inverse problems and imaging, machine learning and pattern recognition
