Michiel Hochstenbach (TU Eindhoven): Some recent trends in the Mathematics of Data Science
We will review some recent developments in some intriguing problems and methods, with lots of applications in Math and Data Science:

How to select the most relevant features and data points from a data matrix? We will review various matrix decompositions, ranging from SVD/PCA to the CUR decomposition, where approximations are in terms of data points.

How can we effectively reduce the dimension when our data points are labeled? We will review linear discriminant analysis and the trace ratio method.

Gradient methods for highdimensional nonlinear optimization. In many problems we have to minimize a difficult loss function. How can we choose the stepsize in gradient methods effectively?
This is joint work with PhD students Giulia Ferrandi and Perfect Gidisu.