Nijenborgh 9, 9747 AG Groningen, NL
m dot biehl at rug dot nl
Room 5161.0584 Tel +31 50 363 3997
meikelbiehl at gmail dot com
Honorary Professor of Machine Learning, University of Birmingham
Center for Systems Modelling and Quantitative Biomedicine
Research:
+++ new +++
list of publications
(pdf and bibtex, including links)
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
June 2026:
Two publications:
April 2026:
March 2026:
Just published
(online, open access) in Neurocomputing:
Any vacancies, available internships, etc. will be
announced properly through the usual channels and on this website.
More than 8500 downloads (UGP and PURE) of
"The Shallow and the Deep"
since its open access publication end of September 2023.
Enhanced broad-band intermuscular coherence in myoclonus: a targeted characterization study
and
Machine learning based EMG analysis of intermuscular coherence and cumulant density in tremor and myoclonus
both with first author Elina van den Brandhof
are available at Clinical Neurophysiology
Oral presentation at
ESANN 2026 in Bruges/Belgium (22-24 April):
Autoencoders versus PCA for feature extraction
in FDG PET scans in neurodegenerative diseases
by Roland Veen, Sofie Lövdal (joint first authors), Kaitlin Vos,
Ciro Setolino, Sanne Meles and Michael Biehl.
The final version of the Nature Machine Intelligence
paper
Aligning generalization between humans and machines
is now publically available at pure.rug.nl
FA(IR)^2MA-GMLVQ - A hidden-feature-bias mitigation approach for fairness in classification learning base on generalized matrix learning vector quantization
by M Kaden, R Schubert, J Voigt, L Reuss, A Engelsberger, S Lövdal,
E van den Brandhof, M Biehl, T Villmann
Unsolicited
applications for internships, PhD or PostDoc positions etc.
will probably remain unanswered.