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Michael Biehl
Professor in Computer Science,
Machine Learning and Computational Intelligence

University of Groningen
Bernoulli Institute
Computer Science Department
Intelligent Systems Group
postal:   P.O. Box 407, 9700 AK Groningen, NL
visitors: Nijenborgh 9, 9747 AG Groningen, NL
              Room # 584, Bernoulliborg
Tel:        (+31) (0)50 363 3997    (Fax: ... 3800)                                                            
e-mail (official):     m dot biehl at rug dot nl
e-mail (private):    meikelbiehl at gmail dot com


Please note:
Should any open positions (internships, PhD etc.) become available in my working group, I will announce them here and through the usual channels. I will not reply to unsolicited requests individually.


News and events:

March 27 and 28, 2019: The second
Workshop on Intelligent Systems and Computational Intelligence (WISCI 2019)
will take place at the Bernoulli Institute, University of Groningen.
See the announcement for details of the program.
Contact: M. Biehl (m.biehl@rug.nl)

April 2019: Special Session at ESANN 2019
Statistical Physics of Learning and Inference (click for details)
Organizers: M. Biehl, N. Caticha, M. Opper, T. Villmann

January 2019: Applications of Intelligent Systems, APPIS 2019
has been held in Las Palmas de Gran Canaria, Spain, 7-12 January 2019.
M. Biehl presented a talk on
Machine Learning for the early detection of crop plant diseases
and gave a turorial on Prototype-based machine learning


Recent journal publications

March 2019:
Learning vector quantization and relevances in complex coefficient space
M. Straat, M. Kaden, M. Gay, T. Villmann, A. Lampe, U. Seiffert, M. Biehl, F. Melchert
Neural Computing and Applications, online, open access (2019).

February 2019:
Galaxy classification: A machine learning analysis of GAMA catalogue data
A. Nolte, L. Wang, M. Bilicki, B. Holwerda, M. Biehl.
Neurocomputing, in press (2019), available online.

December 2018:
Effect estimate comparison between the prescription sequence symmetry analysis (PSSA) and parallel group study designs: A systematic review, D.L. Idema, Y. Wang, M. Biehl, P.L. Horvatovich, E. Hak
PLoS ONE 13(12): e0208389 (2018, open access)

October 2018:
Statistical Mechanics of On-Line Learning Under Concept Drift
M. Straat, F. Abadi, C. Göpfert, B. Hammer, M. Biehl.
Entropy 20(10): 775 (2018) available online (open access)


Available code (GMLVQ and variants):

Python code (scikit-learn compatible)
A collection for LVQ variants
including GMLVQ is avalailable now (thanks to our colleagues at CITEC, Bielefeld University!)
direct download (zip)    github (including scikit-learn)

A Jave plug-in for WEKA
is available from our colleagues at the CI Group Mittweida
- github repository

GMLVQ demo code in Matlab (TM):
A collection of no-nonsense code in Matlab (TM) is available for
Generalized Matrix Relevance LVQ (GMLVQ), see here.
A more complete and more sophisticated Matlab (TM) implementation of
GMLVQ and several variants (due to K. Bunte) is available here.


Objective measures of scientific excellence :-) (pick your favorite)
 5741: citations in scholar.google (21.8 per entry, March 2019)
 2712: citations in web-of-science (20.9 per article, March 2019)
 106: i-10 index in scholar.google (March 2019)
 83: May index (May-factor) 2018/19 (May 2018)
 36: Hirsch index (h-index, h-factor) according to scholar.google (March 2019)
 25: Hirsch index (h-index, h-factor) according to web-of-science (March 2019)
 21: Publications among the 10% (or better) most frequently cited
         of their year and discipline (2007-2017 only) according to Web of Science (Jan. 2018)
 5: seminars co-organized at MPI für Physik komplexer Systeme, Dresden
 4: seminars co-organized at Schloss Dagstuhl (most recent one in June 2016)
 3: Erdös number (pretty much constant, recently)

For slightly more detailed information :-) check out my publications (navigation bar on the left)
Many "services" provide unauthorized and incorrect research or publication profiles. I occasionally check the ones listed in the menu bar (left), but for (almost) complete lists of my publications, citation analyses, frequently cited papers etc. I strongly recommend my Scholar Google profile or 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, e.g., medical data 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: dislocations, self-organized nano-structures, surface alloys

An overview of research in growth and surfaces