click here, if navigation frame does not appear on the left (or more than once)

Michael Biehl
Professor in Computer Science / Machine Learning

University of Groningen
Johann Bernoulli Institute for
Mathematics and Computer Science
Intelligent Systems Group

postal:   P.O. Box 407, 9700 AK Groningen, The Netherlands
visitors: Nijenborgh 9, 9747 AG Groningen, The Netherlands

Room # 584, Bernoulliborg

Tel:    (+31) (0)50 363 3997      Fax:   (+31) (0)50 363 3800     
e-mail (official):             m dot biehl at rug dot nl
e-mail (private):            meikelbiehl at gmail dot com


Deadline: July 14 2017
Applications for PhD positions at the University of Groningen Center for Data Science and Systems Complexity can be submitted until July 14 via the application site. 10 PhD positions are offered, applicants should express their interest in one of 18 potential projects, see here for the project descriptions.
M. Biehl will be part of the supervision team for project number 18, which is entitled
Methods for automated robust analysis of astronomical data.

March 3, 2017: Matthias Gay defended his PhD thesis
Sparsity and random projections in time-frequency-based communication systems:
Theory and algorithm design for the OFDM physical layer

Congratulations, Dr. Gay!

January 2017: GMLVQ demo code (version 2.3) available
A slightly revised (minor bug fixed) collection of GMLVQ demo code in Matlab (TM) is available for Generalized Matrix Relevance LVQ (GMLVQ), see here. Also note that a more complete and more sophisticated Matlab (TM) implementation of GMLVQ and its variants is available here.

Upcoming and recent events:

June 2017: WSOM+ 2017, Nancy/France
Four contributions have been accepted for publication and were presented at the WSOM+ 2017:
Prototypes and Matrix Relevance Learning in Complex Fourier Space
M. Straat, M. Kaden, M. Gay, T. Villmann, A. Lample, U. Seiffert, M. Biehl, F. Melchert
Empirical Evaluation of Gradient Methods for Matrix Learning Vector Quantizationa
M. LeKander, M. Biehl, H. de Vries
Adaptive Basis Functions for Prototype-based Classification of Functional Data
G. Bani, U. Seiffert, M. Biehl, F. Melchert
Fusion of Deep Learning Architectures, Multilayer Feedforward Networks and Learning Vector Quantizers for Deep Classification Learning
T. Villmann, M. Biehl, A. Villmann, S. Saralajew

June 2017: BrainComp 2017
At the 3rd Intl. Wokshop on Brain Inspired Computing, Cetraro/Italy,
M. Biehl presented a talk entitled
Lifelong (machine) learning of drifting concepts
and gave a tutorial on
Prototype-based models in machine learning

June 2017: AlCoB 2017
M. Biehl presented an invited talk on at the 4th Intl. Conf. on Algorithms for Computational Biology:
Biomedical applications of prototype based classifiers and relevance learning
slides (ppsx)     (author generated preprint)
Final version published in:
Algorithms for Computational Biology. 4th International Conference.
D. Figueiredo, C. Martin-Vide, D. Pratas, M.A. Vega-Rodriguez (eds.)
Springer, 2017. Doi: 10.1007/978-3-319-58163-7_1

Most recent journal publications:

March 2017: The artice
Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism
by W. Arlt et al., has been published in
J. of Clinical Investigation, JCI Insight 2(8) 2017

December 2016: The article
Differentiating Early and Late Stage Parkinson's Disease Patients from Healthy Controls
D. Mudali, M. Biehl, S.K. Meles, R.J. Renken, D. Garcia-Garcia, P. Clavero, J. Arbizu, J.A. Obeso, M.C. Rodriguez-Oroz, K. Leenders, and J.B.T.M. Roerdink
appeared in J. of Biomedical Engineering and Medical Imaging 3(6): 33-43 (2016)

August 2016: The article
Learning Vector Quantization classifiers for ROC optimization
T. Villmann, M. Kaden, W. Hermann, M. Biehl
is published (online first) in Computational Statistics (2016)

Very objective measures of scientific excellence (pick your favorite)

 32: Hirsch index (h-index, h-factor) according to
 22: Hirsch index according to web-of-science (as of July 1st, 2017)
 16: Publications among the 10% (or better) most frequently cited of their year and discipline (2006-2016 only)
        according to Web of Science, November 2016
 3: Erdös number
 3: active Erasmus Socrates bilateral agreements
 5: seminars co-organized at MPI für Physik komplexer Systeme, Dresden (most recent one in September 2012)
 4: seminars co-organized at Schloss Dagstuhl (most recent one in June 2016)

For slightly more detailed information :-) check out my publications (navigation bar on the left)
I do not endorse incorrect and unauthorized profiles provided by 'services' like Arnetminer or Microsoft Academic Search. For (almost complete) lists of my publications, citation analyses, frequently cited papers etc. please consult my Scholar Google profile or visit the Web-of-Science based Researcher ID

Research topics:         (see publications for details)

Theory, algorithm development, and applications of machine learning
· computational intelligence, supervised learning (classification, regression), unsupervised learning (e.g. clustering)
· machine learning in, for instance, medical data/image analysis, tumor classification, bioinformatics, astronomy
· non-standard distance measures, adaptive metrics in supervised learning, feature selection and relevance learning
· low-dimensional representation and discriminative visualization of labeled data sets
· mathematical analysis, design and optimization of training algorithms in artificial intelligence

An overview of my earlier contributions to the field of machine learning

Scientific Computing, Modelling and Simulation
· equilibrium and off-equilibrium Kinetic Monte Carlo (KMC) simulations
· theory and modelling of crystal growth, thin films and surfaces
· off-lattice simulation of hetero-epitaxial growth:   Stranski-Krastanov growth, misfit dislocations, self-organized nano-structures, surface alloys

An overview of research in growth and surfaces