EDUCATION

From the era of Newton, fluid dynamics has been the Queen of Applied Mathematics. While this area is still of central importance, a new era of applications has emerged with high dimensional data and its analysis becoming the center of attention.

This course deals with the notion of mathematical modeling and the notion of added value.

This course is intended for advanced students with solid backgrounds in linear algebra who are interested in the mathematical analysis of large data sets.

This course is intended for advanced students seeking to explore the power of mathematics applied to real world problems. Previous editions have included working with SIEMENS on failure prediction, Hewlett Packard on signature recognition in images.

This course concerns geometry, topology and algebra with a view towards data analysis.

Techniques in optimization are extremely useful for characterizing patterns in data. The singular value decomposition is an example of an optimization problem where the solution is in closed form. Linear programming is a useful tool for constructing sparse representations of patterns. Nonlinear programming can reveal relationships in data. Convex optimization allows one to find low-rank approximations in a general setting.