Univ.-Prof. Dr. Philipp Grohs
Professorship for Mathematical Data Science at the Faculty of Mathematics
Contact Philipp Grohs
Curriculum Vitae:
2006 MSc in Technical Mathematics Faculty of Mathematics and Geoinformation, Vienna University of Technology, Austria.
2006-2007 University Assistant (prae-Doc) Research Unit Industrial Geometry, Vienna University of Technology, Austria. Mentor: Prof. Johannes Wallner, Prof. Helmut Pottmann.
2007 PhD in Mathematics, Faculty of Mathematics and Geoinformation, Vienna University of Technology, Austria
2007-2009 Postdoctoral Researcher Institute of Geometry, Graz University of Technology, Austria
2009-2010 Postdoctoral Researcher Geometric Modeling and Scientific Visualization Center, KAUST, Saudi Arabia
2011 Habilitation in Mathematics, Faculty of Mathematics, Physics and Geodesy, TU Graz, Austria
2010-2011 Postdoctoral Researcher Department of Mathematics, ETH Zürich, Switzerland
2011-2016 Assistant Professor for Applied Mathematics (non-TT) Department of Mathematics, ETH Züurich, Switzerland
2016-2016 Assistant Professor for Applied Mathematics (TT) Faculty of Mathematics/Department of Mathematics, University of Vienna, Austria
2016-2019 Associate Professor for Applied Mathematics, Faculty of Mathematics/Department of Mathematics, University of Vienna, Austria
2019-now Full Professor (temporary for five years, after Par. 99/1) for Applied Mathematics Faculty of Mathematics/Department of Mathematics, University of Vienna, Austria
2019-now Head of the Research Group "Mathematical Data Science" Johann Radon Institute, Austrian Academy of Sciences, Austria
2020-now Head of the Research Network "Data Science" University of Vienna, Austria
Since February 2022 Professor at the Department of Mathematics, University of Vienna
Research areas:
* Construction and analysis of numerical methods for stable phase reconstruction
* Analysis of 'deep learning' algorithms
* Construction and analysis of numerical methods for solving partial differential equations
"Data-driven methods such as machine learning and artificial intelligence are playing an increasingly important role in our society. It is of central importance to be able to guarantee that these methods reliably solve the problems for which they were designed. In my research group we are developing mathematical foundations for such Guarantees deliver and use the insights gained to construct efficient, stable and interpretable AI algorithms." (Philip Grohs)