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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


News:

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.

15.12.2016 (deadline): 14 PhD positions in Computer Science and Astronomy
SUNDIAL SUrvey Network for Deep Imaging Analysis & Learning) is an ambitious interdisciplinary network of of nine European research groups. The aim of the network is to develop novel algorithms to study the very large databases coming from current-day telescopes to better understand galaxy formation and evolution, and to prepare for the huge missions of the next decade.


Upcoming and recent events:

June 2017: WSOM+ 2017, Nancy/France
Three contributions have been accepted for publication and will be 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

June 2017: AlCoB 2017
M. Biehl will present an invited talk on
Biomedical applications of prototype based classifiers and relevance learning
(author generated preprint, final version to be published by Springer)
at the 4th International Conference on Algorithms for Computational Biology

May 2017: IJCNN Special Session
A special session addressing (click title for further information)
Interpretable models in machine learning for advanced data analysis
will be organized by M. Biehl and T. Villmann
at the IJCNN 2017: International Joint Conference on Neural Networks, Anchorage/Alaska.
Submission closed December 1, see here for general information and updates.

April 2017: ESANN Special Session
A special session addressing (click title for further information)
Biomedical data analysis in translational research: integration of expert knowledge and interpretable models
will be organized by G. Bhanot, M. Biehl, T. Villmann, and D. Zühlke at the
25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Submission closed November 26, see here for details.

April 2017: ICT.OPEN
Sreejita Ghosh presented the paper entitled
Computer aided diagnosis under the influence of heterogeneous data and imbalanced classes
at the 2017 ICT.OPEN conference and was awarded the health track best poster award.


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)

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 scholar.google
 21: Hirsch index according to web-of-science
 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 (it decreased by 1, recently!)
 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