In this project, we will develop methods of data analysis that are simultaneously able to (i) model non-linear dependencies and (ii) tolerate large amounts of contaminated and faulty data (a property known as robustness). Both properties are highly called for in the analysis of the complex data sets encountered today. Our main focus will be on constructing estimators of location and scatter and on using them to develop robust non-linear dimension reduction. The developed methods will be evaluated both theoretically and through their capabilities in data analysis.

2022

2027