Prof. Dr. Peter Zaspel

W2 Professor in Software for Data-Intensive Applications

Bergische Universität Wuppertal
Fakultät für Mathematik und Naturwissenschaften
Wissenschaftliches Rechnen und Hochleistungsrechnen

Gaußstraße 20
42119 Wuppertal, Germany

Tel: +49 202 439 2668
Email: zaspel at
Office: G.14.13


P. Zaspel. High-dimensional approximation for large-scale applications, Habilitation Thesis in Mathematics, University of Basel, Switzerland, April 2021.

P. Zaspel. Parallel RBF Kernel-Based Stochastic Collocation for Large-Scale Random PDEs, PhD Thesis, Institute for Numerical Simulation, University of Bonn, Germany, April 2015

P. Zaspel. Zweiphasige Navier-Stokes Fluidsimulationen in Maya: Konfiguration, Visualisierung und Animation. Diploma Thesis, Institute for Numerical Simulation, University of Bonn, April 2009.

Academic career path

since July 2023W2 Professorship in Software for Data-Intensive Applications, Bergische Universität Wuppertal, Wuppertal, Germany
March 2022 – June 2023Assistant Professor of Computer Science (Machine Learning), Jacobs University Bremen gGmbH, Bremen, Germany
Aug. 2019 – Feb. 2022Interim Professor of Computer Science (Machine Learning), Jacobs University Bremen gGmbH, Bremen, Germany
2017 – 2019Postdoc, Department of Mathematics and Computer Science at the University of Basel (DMI, topical area: mathematics), Basel, Switzerland.
2015 – 2017Postdoc, Heidelberg Institute for Theoretical Studies: HITS gGmbH, Heidelberg, Germany.
2015 – 2017Postdoc (associated), Interdisziplinary Center for Scientific Computing (IWR, University of Heidelberg), Heidelberg, Germany.
2009 – 2015Research assistant, Institute for Numerical Simulation (INS, University of Bonn), Bonn, Germany.


2019 – 2021Habilitation in mathematics, University of Basel, Basel, Switzerland.
2009 – 2015PhD student in applied mathematics, University of Bonn, Bonn, Germany.
2004 – 2009Diplom student in computer science, University of Bonn, Bonn, Germany.

Invited Talks

Augmenting the explanatory power of predictions by uncertainty quantification,
2nd Workshop on Embedded Machine Learning – WEML2018, Heidelberg University, Nov 8, 2018.

Meshfree and multi-index approximations for parametric real-world problems,
Seminar on Uncertainty Quantification, RWTH Aachen, Aachen, Germany, August 29, 2018.

Optimal-complexity kernel-based stochastic collocation with application in fluid mechanics,
Seminar of the “Mathematics in Computational Science and Engineering” group, on invite by Prof. Dr. Fabio Nobile, EPFL, Lausanne, Switzerland, October 24, 2017.

Scalable solvers for meshless methods on many-core clusters,
QUIET 2017 – Quantification of Uncertainty: Improving Efficiency and Technology, SISSA, International School for Advanced Studies, Trieste, Italy, July 18-21, 2017.

H-matrices on many-core hardware with applications in parametric PDE’s,
Colloquium of the Faculty of Engineering, on invite by Prof. Dr. Steffen Börm, University of Kiel, December 9, 2016.

Algorithmic patterns for hierarchical matrices on many-core processors,
Seminar in Numerical Analysis, on invite by Prof. Dr. Helmut Harbrecht, University of Basel, September 18, 2016.