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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.
If you have acquired your own funding or scholarship for a PhD project, please send an e-mail with subject Supervision for funded PhD together with a CV and a detailed research proposal.


News and events:

October 10, 2018: Aleke Nolte has received the Best Poster Award
at the NWO Symposium on Applied Computational Sciences (ACOS) in Eindhoven, The Netherlands,
for the contribution Galaxy classification: a machine learning analysis of GAMA catalogue data
(link to a related publication)

October, 2018: Journal publication available (open access):
Statistical Mechanics of On-Line Learning Under Concept Drift
M. Straat, F. Abadi, C. Göpfert, B. Hammer, M. Biehl.
Entropy 20(10): 775 (2018), doi: 10.3390/e20100775

November 2018: The 30th Canary Islands Winter School of Astrophyiscs
will be organized at the IAC in San Cristobal de La Laguna (Tenerife).
M. Biehl will present lectures on Supervised learning: classification and regression

January 2019: Applications of Intelligent Systems, APPIS 2019
will be held in Las Palmas de Gran Canaria, Spain, 7-12 January 2019.
M. Biehl will present a turorial on Prototype-based machine learning

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


Available code:

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

GMLVQ demo code in Matlab (TM):
A collection of no-nonsense GMLVQ 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 is available here.


Objective measures of scientific excellence :-) (pick your favorite)
 5493: citations in scholar.google (21.6 per entry, October 2018)
 2570: citations in web-of-science (20.1 per article, October 2018)
 106: i-10 index in scholar.google (October 2018)
 83: May index (May-factor) 2018/19 (May 2018)
 34: Hirsch index (h-index, h-factor) according to scholar.google (October 2018)
 24: Hirsch index (h-index, h-factor) according to web-of-science (October 2018)
 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