Mr Prof. Dr. Peter Zaspel
Biography
| seit Juli 2023 | W2 Professur Software für Daten-intensive Anwendungen, Bergische Universität Wuppertal, Wuppertal, Deutschland |
| März 2022 – Juni 2023 | Assistant Professor of Computer Science (Machine Learning), Jacobs University Bremen gGmbH, Bremen, Deutschland |
| Aug. 2019 – Feb. 2022 | Interim Professor of Computer Science (Machine Learning), Jacobs University Bremen gGmbH, Bremen, Deutschland |
| 2017 – 2019 | Postdoc, Departement für Mathematik und Informatik, Universität Basel, Schweiz. |
| 2015 – 2017 | Postdoc, Heidelberg Institut für Theoretische Studien: HITS gGmbH, Heidelberg, Deutschland. |
| 2015 – 2017 | Postdoc (assoziiert), Interdisziplinäres Zentrum für Wissenschaftliches Rechnen (IWR, Universität Heidelberg), Heidelberg, Deutschland |
| 2009 – 2015 | Wissenschaftlicher Mitarbeiter, Institute für Numerische Simulation (INS, Universität Bonn), Bonn, Deutschland. |
Education
| 2019 – 2021 | Habilitation in Mathematik, Universität Basel, Basel, Schweiz. |
| 2009 – 2015 | Doktorand in angewandter Mathematik, Universität Bonn, Bonn, Deutschland. |
| 2004 – 2009 | Diplom Student in Informatik, Universität Bonn, Bonn, Deutschland. |
Ongoing research projects
- DFG project “Multi-fidelity, Active Learning Strategies for Exciton Transfer Among Adsorbed Molecules” (duration 2022-2025)
Research project within the DFG SPP 2363 “Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning” (joint project with Ulrich Kleinekathöfer, Physics, Jacobs University Bremen) - MarDATA project “Bayesian Chronology Modelling for Paleoclimate Archives” (duration: 2022-2025)
Helmholtz School for Marine Data Science (joint project with Florian Adolphi, Alfred Wegener Institute). - MarDATA project “Digital Ice Cores: Paleo-Climate reconstruction using Bayesian methods” (duration: 2022-2025)
Helmholtz School for Marine Data Science (joint project with Thomas Laepple, Alfred Wegener Institute & U. Bremen). - DFG project “Excitation Energy Transfer in a Photosynthetic System with more than 100 Million Atoms” (duration: 2021-2024)
Individual Research Grant funded by DFG (joint project with Ulrich Kleinekathöfer, Physics, Jacobs University Bremen)
Research interests
- Machine Learning, Uncertainty Quantification and Big Data
- multi-fidelity machine learning (e.g. by sparse grid combination technique)
- approximate training (low-rank approximation by e.g. hierarchical matrices)
- stochastic collocation, Bayesian inference / data assimilation
- basic research wrt. reproducing kernel Hilbert spaces / Gaussian processes
- High Performance Computing
- numerics / algorithms for many-core processors (e.g. GPUs)
- scalable distributed-memory parallel computing in machine learning and scientific computing
- Interdisciplinary applications
- material science, quantum chemistry (data from DFT, CC, etc.)
- paleo-climate reconstruction (calibration, etc.)
- fluid mechanics (two-phase flows, plasma physics)
- medical imaging (dynamic contrast-enhanced imaging)
Plublications
Publication list
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.
