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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:

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

April 2018: Special Session at ESANN 2018
Machine Learning and Data Analysis in Astroinformatics (click for details)
Organizers: M. Biehl, K. Bunte, G. Longo, P. Tino

March2018: WISCI 2018
At the first Groningen International Workshop on Intelligent Systems and Computational Intelligence
twelve speakers presented recent advances in the development, theory and application of
Machine Learning and Computational Intelligence. See here for the workshop program

January 2018 Applications of Intelligent Systems (APPIS), Las Palmas de Gran Canaria
M. Biehl presented an invited talk with the title
Bio-medical applications of prototytpe-based machine learning
and a contribution entitled
Machine learning analysis of FDG-PET brain images for the diagnosis of neurodegenerative disorders


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)
 5315: citations in scholar.google (20.9 per entry, July 2018)
 2484: citations in web-of-science (19.9 per article, July 2018)
 106: i-10 index in scholar.google (June 2018)
 83: May index (May-factor) 2018/19
 33: Hirsch index (h-index, h-factor) according to scholar.google
 23: Hirsch index according to web-of-science
 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