Assistant Professor

Paulo De Andrade Serra

image
Group / Unit
Stochastics W&I
Building
MetaForum
Floor / room
4.088

Research Profile

Mathematical Statistics is a subfield of statistics where mathematical tools are used to design and study statistical procedures. Within Mathematical Statistics, Paulo’s research focuses on non-parametric (high- or infinite dimensional) models. Such large models are useful to avoid having to make potentially restrictive assumptions. Paulo work mainly with three different types of approaches: penalised estimation (used to reconstruct complex signals from observations), recursive estimation (a tool for online learning), and Bayesian estimation (a powerful tool for statistical learning that allows expert knowledge to be incorporated into the inference in a natural way).  Currently Paulo focuses on the use of these techniques in medical applications, working jointly with researchers at Philips and other departments of the TU/e. He is also involved with the SMM research program of the Data Science Center Eindhoven (DSC/e) and does external consulting work.

Academic Background

Paulo Serra obtained Licenciatura in Applied Mathematics at New University of Lisbon, Portugal, followed by an MSc from Utrecht University, and a PhD from the Eindhoven University of Technology. He has been a postdoc at the Korteweg-de Vries Institute, of the University of Amsterdam. Before that, he spent two years as a postdoc at the Institute for Mathematical Stochastics of the University of Göttingen, in Germany. Earlier, he was a junior researcher for a year at CA3 group at UNINOVA, in Portugal, working on project NOMDIS developed for ESA/ESOC. Paulo has contributed to several publications, book chapters, and conference proceedings. Since 2016 Paulo has been an Assistant Professor at the Stochastics group of the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e).

Educational Activities

  • Linear statistical models
  • Mathematical modelling
  • Probability and statistics
  • Mathematics 1
  • Programming and modelling

Ancillary Activities

No ancillary activities