Sommersemester 2026

Nicolás García Trillos is an associate professor at the Department of Statistics at the University of Wisconsin Madison. His research work lies at the intersection of applied analysis, applied probability, machine learning, and statistics. Some of his current research interests include adversarial machine learning, operator learning, and optimal transport in statistics.

He received his PhD degree from the Department of Mathematical Sciences at Carnegie Mellon University in 2015. In his dissertation, he investigated variational problems arising in graph-based unsupervised learning, like min-cut problems on random geometric graphs, and developed tools motivated by ideas in the calculus of variations and optimal transport theory to study their large-sample consistency. Between 2015 and 2018, he was a Prager Assistant Professor at the Division of Applied Mathematics of Brown University. Since 2018, he has been at the University of Wisconsin Madison.

Prof. García Trillos's main current research interest is in the development of new frameworks for inference in statistics and machine learning. He is particularly interested in using geometric methods to propose estimators with low sensitivity to certain types of data contamination, and to obtain sharp tradeoffs between those notions of sensitivity and other desirable performance criteria for estimators.

In the summer semester 2026 Nicolás García Trillos will teach the course 'Statistical analysis of graph-based learning on manifolds (Giovanni Prodi Lecture)' (lecture with exercises).