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


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

University of Groningen
Johann Bernoulli Institute for
Mathematics and Computer Science
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:   (+31) (0)50 363 3800                                                             Where is this?
e-mail (official):             m dot biehl at rug dot nl
e-mail (private):            meikelbiehl at gmail dot com


Please note:
Any open positions (internships, PhD etc.) in my working group would be announced here and through the usual channels. I will not be able to reply to all requests individually.


News:

December 2017: A scikit-learn compatible collection of Python code for LVQ variants
including GMLVQ is avalailable now (thanks to our colleagues at CITEC, Bielefeld University!)
direct download (zip)

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:

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

January 2018 Applications of Intelligent Systems (APPIS), Las Palmas de Gran Canaria
M. Biehl will present a tutorial 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

November 2017: Astroinformatics 2017, Cape Town, South Africa
M. Biehl presented an invited talk with the title
Prototype-based machine learning in unsupervised and supervised machine learning (download ppsx)


Most recent journal publications:

March 2017: The article
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)


Very objective measures of scientific excellence :-) (pick your favorite)

 5016: citations in scholar.google (20.5 per entry, Jan. 2018)
 2265: citations in web-of-science (18.1 per entry, Jan. 2018)
 32: Hirsch index (h-index, h-factor) according to scholar.google
 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 (Nov. 2016)
 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:   Stranski-Krastanov growth, misfit dislocations, self-organized nano-structures, surface alloys

An overview of research in growth and surfaces