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CV Hilbert Johan Kappen
This CV summarizes my main activities and achievements over the last 10 years. Full list of publications is found
on-line at www.snn.ru.nl/~bertk.
Personal data:
Born July 17, 1958 in Maassluis, the Netherlands. Married, 3 children
Education:
• 1976-1983: Master in theoretical high energy physics, State University Groningen, The Netherlands. Thesis
topic: Study of magnetic mono-poles in classical field theory.
• 1983-1986: Ph.D. in theoretical high energy physics, Rockefeller University, New York. Thesis topic: Study of
radiative corrections in the standard model of the weak interactions and in low energy super-gravity theories.
• 1987: Computer Science degree (engineering level) as part of Philips Research intensive training program.
Appointments:
• 1987-1989: Scientist at the Philips Research Laboratories, Eindhoven, the Netherlands. Investigation of efficient
computer algorithms for various combinatoric optimization problems in VLSI design.
• 1989- present: Researcher at the department of Biophysics, University of Nijmegen, the Netherlands. Head
of research group of 10 researchers. Research on approximate inference and stochastic optimal control theory,
machine learning, computational neuro-science, bio-informatics and various applications. I teach Control Theory,
Machine Learning and Computational Neuroscience as part of the physics curriculum at both Bachelor and
Master level.
– 1997: Appointment as associate professor of neural networks and machine learning
– 2005: Appointment as full professor of neural networks and machine learning
– Vice director (since 1993) and director (since 2004) of the Dutch Foundation of Neural Networks (SNN). SNN
is the national consortium of research groups on neural networks and machine learning in the Netherlands.
Visiting appointments:
• 2005: Sabatical at UC Berkeley with Michael Jordan (four months)
• 2011: Sabatical at UCL Gatsby Unit with Peter Dayan and Yee Why Teh (3 months)
Supervision of PhD theses
Tom Heskes (PhD 1993); Martijn Leisink (PhD 2004); Taylan Cemgil (PhD 2004); Joris Mooij (PhD Cum Laude 2008);
Kees Albers (PhD 2008); Bart van den Broek (PhD 2012); Mohammad Azar (PhD 2012); Alberto Llera (2009-2013);
Sep Thijssen (2010-2014);
Current postdocs
Wim Wiegerinck, Vicenc Gomez, Kevin Sharpe, Joris Bierkens.
Editor
Editorial board of Journal of Statistical Mechanics (2004), Journal of Natural Computing (2000)
Grant Reviewing
Dutch National Science Foundation (NWO), Economic and Social Research Council (UK), European Commission,
European Research Council, German Ministry of Research and Technology (BMFT), National Science Foundation
(US), NATO Research Grants, Swedisch Foundation for Strategic Research, Swiss National Science Foundation (SNSF)
Journal and Conference Refereeing
AIStats, Biological Cybernetics, Europhysics Letter, IEEE Information Theory, IEEE Man Systems Cybernetics, IEEE
Neural Networks, International Conference on Machine Learning, International Journal Robust Nonlinear Control,
International Journal Control, Journal of AI Research, Journal of Machine Learning Research, Journal of Neuroscience,
Journal of Physics A: Math General, Machine Learning Journal, Nature, Neural Computation, Neural Information
Processing Systems, Neural Networks, Physical Review E, Physical Review Letters, PLOS Computational Biology,
Proceedings National Academy of Sciences, Uncertainty in AI,
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Current external funding
Pascal 2 Member of the EU network of excellence ”Pattern Analysis, Statistical Modeling and Computational
Learning” (2009-2012).
Brain Computer Interface Two PhD positions for four years for research on Brain Computer Interface (20092013). Funded by the national BrainGain initiative.
Genetic association study A 3 year postdoc position for machine learning of genetic data analysis (in collaboration
with Barbara Franke, Donders Institute, 2011-2013). Funded by Donders’s Institute Radboud University Nijmegen.
Cooperative agents A 4 year PhD position for cooperative agent modeling using control theory (2010-2014).
Funded by Thales Research Netherlands.
Genetic association study A 3 year postdoc position for machine learning of genetic and fMRI data analysis (in
collaboration with Jan Buitelaar, Donders Institute). Funded by NWO, 2011-2013.
Optimal control theory A 3 year postdoc position and a 4 year PhD position in the ”Composing learning systems
for Artificial Cognitive Systems” (CompLACS) project. Coordinator is John Shawe-Taylor, UCL. The project aims
to develop fundamental machine learning methods to build intelligent cognitive systems. My part of the project aims
to develop efficient methods for stochastic optimal control theory. Funded by EU FP7, 2011-2014.
Neural mechanism for control Two 4 year PhD positions in the Marie-Curie ITN project ”Neural Engineering
Transformative Technologies” (NETT). Funded by EU FP7, 2012-2016
Research Profile
The research interests of Bert Kappen (BK) lie at the interface between statistical physics, computer science, computational biology, control theory and artificial intelligence. Due to the stochastic nature of problems in artificial and
natural intelligence and the large number of variables involved, computations tend to be intractable. This poses a
fundamental problem for algorithms for vision, motor control, memory or expert systems. BK has developed many
novel approximate inference methods inspired by methods from statistical physics. BK is author of about 80 peer
reviewed articles in scientific journals and leading conferences.
Main scientific achievements:
• BK has made leading contributions to the development and use of novel approximate inference methods for
application in intelligent systems and machine learning, using insights from statistical physics. Examples are the
TAP approximation, linear response theory, Cluster Variation Method, and loop corrected belief propagation.
• BK (with J Torres) has pioneered the mean field analysis of stochastic neural networks with dynamical synapses,
revealing up and down states and rapid switching.
• BK has identified a novel class of non-linear stochastic control problems that can be solved using path integrals.
This approach has been adapted by leading robotics groups and is recognized as an important novel approach
to stochastic control. This work is the basis for the present proposal.
• The early work of BK on mean field theory for asymmetric stochastic neural networks is at the basis of much
current research on finding connectivity patterns in neural circuits.
International recognition and diffusion In virtue of his body of work, BK has been invited as visiting professor
in prestigious research institutions such as UC Berkeley and Tokyo University. In addition, he has been appointed as
honorary faculty at the Gatsby Unit for Computational Neuroscience at Univesity College London. The group of BK
is the leading research group on machine learning in the Netherlands and has a strong international reputation.
Ability to establish new interdisciplinary approaches BK’s novel theory for path integral control is truly
interdisciplinary, linking control theory, machine learning and statistical physics and is being applied in robotics. An
international meeting that brought together leaders from these diverse fields was held in september 2012 www.snn.
ru.nl/cyberstat_granada.
BK, in collaboration with medical experts, has developed a Bayesian medical expert system, including approximate
inference methods, and he has co-founded a company to commercialize this system.
The SNN has a long reputation of successful application of neural network and machine learning methods in
collaboration with numerous industrial partners (see also Examples of leadership below).
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10-Year Track-Record
10 selected publications
Because of the relative importance of conference publications in the field of machine learning, citation analysis from
both Web of Knowledge (H-index=13) and Google Citation 1 (H-index=25) is given. Web of Knowledge and Google
citations between brackets.
• L. Pantic, J.J. Torres, H.J. Kappen, and C.C.A.M. Gielen. Associative memory with dynamic synapses. Neural
Computation, 14:2903–2923, 2002. (60,81)
• A. T. Cemgil and H. J. Kappen. Monte Carlo methods for tempo tracking and rhythm quantization. Journal
of Artificial Intelligence Research, 18:45–81, 2003. (31,96)
• T. Heskes, K. Albers, and H.J. Kappen. Approximate inference and constraint optimisation. In Proceedings
UAI, pages 313–320, 2003. (-,72)
• M. Leisink and H.J. Kappen. Bound propagation. Journal of Artificial Intelligence Research, 19:139–154, 2003.
(3,30)
• H.J. Kappen. A linear theory for control of non-linear stochastic systems. Physical Review Letters, 95:200201,
2005. (12,72)
• H.J. Kappen. Path integrals and symmetry breaking for optimal control theory. Journal of statistical mechanics:
theory and Experiment, page P11011, 2005. (9,56)
• J. Mooij and H.J. Kappen. On the properties of the bethe approximation and loopy belief propagation on binary
networks. Journal of Statistical Mechanics: Theory and Experiment, page P11012, 2005. (13,33)
• J.M. Cortes, J.J. Torres, J. Marro, P.L. Garrido, and H.J Kappen. Effects of fast presynaptic noise in attractor
neural networks. Neural Computation, pages 614–633, 2006. (18,34)
• A. T. Cemgil, H. J. Kappen, and D. Barber. A generative model for music transcription. IEEE Transactions
on Speech and Audio Processing, 14(2):679–694, 2006. (38,98)
• J. Mooij and H.J. Kappen. Sufficient conditions for convergence of the sum-product algorithm. IEEE Information
Theory, 53:4422–4437, 2007. (19,81)
Invited presentations
”Biophysical signal processing”, Conference, Bari Italy 2001;
”Learning”, Snowbird workshop Utah USA 2004;
”Oscillations and Instability; control, near and far from equilibrium in Biology”, Lorentz Center Workshop, Leiden
the Netherlands 2005;
”Cooperative Behaviour in Neural Systems”, Granada Statistical Physics Seminar 2006;
”Nonlinear dynamics and statistical mechanics of Complex systems”, Lavin Ch 2008.
”Approximate Inference in Stochastic Processes and Dynamical Systems”, Cumberland Lodge UK 2008;
”International Conference of Machine Learning”, Helsinki 2008;
”Statistical mechanics of learning and inference”, Nordita Workshop Mariemamn, Finland 2010;
”Statistical physics of complexity, optimization, and systems biology”, Bardonnechia Italy 2011;
”Statistical Mechanics of Inference”, Kavli-Nordita Workshop Trondheim, Norway 2011;
”Causal graphs: linking brain structure to function”, NIPS workshop Granada Spain 2011;
”Bridging statistical physics and optimization, inference and learning”, Les Houches Fr. 2012;
”Optimization and Control for smart grids”, Los Alamos National Laboratory 32nd Annual Conference 2012;
”Statistical Inference in Modelling, Physics and Learning (SIMPLE)”, MPI Dresden, September 2012;
Invited talks at ETH Zürich, Universidad Autonoma Barcelona, University of Lübeck, UC Berkeley, Stanford University, Granada University, Universidad Autonoma de Madrid, Universidad Pompeu Fabra, Barcelona, Cambridge
University, University College London, Xerox Research, TU Berlin, Bogazici University Istambul, Kings College London, Imperial College London, University of Edinburgh.
Organisation of International conferences and schools
• ”Inference and optimization in machine learning and physics” , ETH/Lavin Ch, January 19-23 2005. Proceedings
in Journal of Statistical Mechanics: theory and experiment.
• ”Cooperative Behavior in Neural Systems”, Granada Statistical Physics Seminar 2006. Proceedings American
Institute of Physics.
• ”Nonlinear dynamics and statistical mechanics of Complex systems”, ETH/Lavin Ch, 18-22 January 2008.
1 http://scholar.google.com/citations?user=JSkBINwAAAAJ&hl=en
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• ”Nonlinear Dynamics of Electronic Systems”, ETH/Rapperswil Ch, June 21-24, 2009.
• ”Probabilistic approaches for control and robotics”, Whistler Canada, December 11, 2009
• ”Foundations of non-equilibrium statistical physics”, Granada Statistical Physics Seminar 2010. Proceedings
American Institute of Physics.
• ”Large Scale Problems in Machine Learning”, Summer school ICTP Trieste August 2012
• ”Common concepts in machine learning and statistical physics”, Workshop ICTP Trieste August 2012
• ”Statistical physics of control theory”, Granada Spain September 2012
• ”Physics, Computation, and the Mind Advances and Challenges at Interfaces”, Granada Statistical Physics
Seminar September 2012
International Prizes/Awards
• 1997-2007: NWO (Dutch Science Foundation) PIONIER personal research award entitled ”Knowledge representation with neural networks”. At the time, the PIONIER awards were the most prestigious personal research
awards in the Netherlands. The total subsidy was Mfl. 2 (1 MEuro).
• 2005: UC Berkeley Miller Visiting Professorship
• 2009: Appointed as honorary faculty at Gatsby Computational Neuroscience Unit, University College London.
Major contributions to early careers of excellent researchers
Tom Heskes did his PhD with me in 1993 and was postdoc in my lab until 2000. We jointly developed on-line learning
theory for neural networks as well as the first approximate inference methods in Europe. This forms the basis of his
current work as full professor on Machine Learning at Radboud University.
Joaquin Torres was postdoc with me from 2000-2002. We jointly analysed the information processing properties of
neural networks with dynamical synapses. This forms the basis of his current work as associate professor in statistical
physics at the University of Granada.
Taylan Cemgil did his PhD with me in 2004. We jointly developed the first Bayesian methods for music analysis.
This forms the basis of his current work as assistant professor at Bogazici University, Istanbul, Turkey.
Cees Albers did his PhD with me in 2008. We jointly developed the first application of generalized belief propagation
to genetics. He is now researcher at the Sanger Institute in Cambridge, UK.
Examples of leadership in industrial innovation
Co-founder of Smart Research BV www.smart-research.nl, a company dedicated to the development of machine
learning technology. One of the recent products is Bonaparte www.bonaparte-dvi.com, a DNA identification system
for missing persons.
Co-founder of Promedas BV www.promedas.nl, a company that commercializes the Promedas medical diagnostic
expert system.
Director of SNN, the Dutch Foundation for Neural Networks www.snn.ru.nl. SNN promotes application oriented
research in the Netherlands through the Machine Learning Platform www.mlplatform.nl.
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