- Model Reduction
- Parametrized PDEs
- Parametrized dynamical systems
- Reduced basis methods
- Kernel methods for nonlinear systems
- Adaptive Basis Generation
- POD-Greedy procedures
- Numerical Analysis
- Evolution schemes, FV, LDG-methods
- Conservation laws
- Variational inequalities
- Optimization with PDE constraints
- Kernel methods for function approximation / PDEs
- Greedy Procedures
- Transport problems, fluid dynamics, single-/two-phase flow
- Obstacle problems, Option Pricing
- Geometry parametrization and optimization
- Multiscale problems
- Elastic multibody systems
- Chemical Master Equation
- Fuel cells, Lithium-Ion cells
- Scientific Computing
- Multiresolution visualization
- Numerical Software development
- Machine Learning
- Kernel methods, kernel design
- Support vector machines
- Kernel Fisher / Mahalanobis Discriminants
- Proximity-based learning
- Pattern Recognition
- Feature extraction
- Classifier design
- Image processing
- Handwriting Recognition
- Raman-Spectra Recognition
B. Haasdonk studied Physics, Mathematics and Computer Science at the University of Freiburg from 1995-2000.
After his Diploma Thesis in Numerical Analysis in 2000 he started research in Pattern Recognition and Machine Learning. One particular focus of his work represents kernel methods and kernel design. Since his PhD thesis in 2005, the continuation of this research was partially supported by a scholarship of the German Scientific Exchange Service (DAAD). He extended his focus to the field of model reduction of numerical simulation methods and joined the Applied Mathematics Institute at the University of Freiburg as a Postdoc. He spent some months at the Massachusetts Institute of Technology and moved to the University of Münster in 2007.
In 2009, he has joined the "Excellence Cluster in Simulation Technology" at the University of Stuttgart as Juniorprofessor.
Until 2014 he had obtained 4 projects funded by the Baden Württemberg Stiftung gGmbH. In 2014 B. Haasdonk was appointed a professorship on "Numerical Mathematics" at the Institute of Applied Analysis and Numerical Simulation of the University of Stuttgart, while rejecting calls of the University of Siegen and Clausthal.
From 2014-2018 he serves as a german representative in the Management Committee of the "European Model Reduction Network" funded by the European Union.
IEEE PerCom 2017: Mark Weiser Best Paper Award: Dibak, C., Schmidt, A., Dürr, F., Haasdonk, B., Rothermel, K.: Server-Assisted Interactive Mobile Simulation for Pervasive Applications, 2017
Teaching Award "Beste Aufbauvorlesung" of the Fachschaft Mathematik of the University of Stuttgart for the Lecture "Numerische Mathematik 2"
Teaching Award "Beste Vertiefungsvorlesung" of the Fachschaft Mathematik of the University of Stuttgart for the lecture "Reduced Basis Methods"
Best Paper Award for the contribution: Haasdonk, B., Pekalska, E., Classification with Kernel Mahalanobis Distances. Proc. of 32nd. GfKl Conference, Advances in Data Analysis, Data Handling and Business Intelligence, 2008
Participation in the Awarded Exhibition Hightech Underground 2008
DAAD-ARC research grant
Admittance to the Eliteprogramm für Postdoktorandinnen und Postdoktoranden of the Landesstiftung Baden-Württemberg gGmbH
Prize in SAS Mining Challenge 2003
Best Paper Presentation Award for the contribution: Bahlmann, C., Haasdonk, B., Burkhardt, H., On-Line Handwriting Recognition with Support Vector Machines - A Kernel Approach. IWFHR-8, 2002. (.ps, .pdf)
Förderpreis 2000 des Verbands der Freunde der Universität Freiburg for the best graduation at the Institute of Mathematics
- EU-MORNET, Management Committee Member
- DMV, German Mathematicians Society
- DAGM, German Pattern Recognition Society
- IAPR, International Association for Pattern Recognition
- DHV, German Association of University Professors and Lecturers
- WiR-Ba-Wü, Research network for scientific computing in Baden-Württemberg
- CoSiMOR, Scientific Network on Scale Bridging simulation methods based on order-reduction and co-simulation