Research:
Machine Learning and Computational Intelligence
Theory and algorithm development for neural networks
Learning Vector Quantization and Relevance Learning
Applications in life sciences, biomedical data, astroinformatics
Statistical Physics and Scientific Computing
Theory of neural networks, dynamics of machine learning processes
Monte Carlo simulations of complex systems
Disordered systems, non-equilibrium growth processes
Teaching: Neural Networks and Computational Intelligence Modelling and Simulation Introduction to Machine Learning
+++ +++ breaking news +++ +++
April 23-25, 2025:
Our contributions The Role of the Learning Rate in Layered Neural Networks with ReLU Activation Function
by O. Citton, F. Richert, M. Biehl
Interpretable machine learning for the diagnosis of hyperkinetic movement disorders
by E. van den Brandhof, J.W. Elting, I. Tuitert, M. van der Stouwe,
J. Dalenberg, M. de Koning-Tijssen, M. Biehl
Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach
by M. Kaden, A. Engelsberger, R. Schubert, S. Lövdal,
E. van den Brandhof, M. Biehl, T. Villmann
will be presented at the 33rd
ESANN 2024 and
published in the proceedings.
November 7, 2024: I finally quit Twitter/X today. It feels very good.
Any vacancies, available internships, etc. will be
announced properly through the usual channels and on this website. Note that
I will not reply to unspecific requests or unsolicited
applications for internships, PhD positions etc.
