Wim Wiegerinck


Foundation for Neural Networks (SNN)
Biophysics, Donders Institute for Brain, Cognition and Behaviour
Radboud University Nijmegen
Heyendaalseweg 135
Room 00.833 6525 AJ Nijmegen
The Netherlands

+31 24 3615040

and then add @science.ru.nl

SNN Machine Learning seminars

  • Visit to Interpol, to install Bonaparte!

Current interests
My main scientific interests are
  • Machine learning:
    • Bayesian networks, Bayesian learning, MCMC methods, Gaussian Processes, Bayesian optimization, neural networks, deep learning.
    • A not-too-technical explanation of Bayesian methods and machine learning can be found in >Dutch and English
    • Smart Research BV: Applications of neural networks and machine learning
    • Big4Data: our new commercial name. We do still Smart Research, but now we go Big4Data!
    • Methods for modeling Bayesian networks
    • Deterministic methods for approximate inference in intractible graphical models (mean field, belief propagation, EP)
    • Multi-agent planning and control
    • Supermodeling! Applying machine learning to models with 100,000s of variables to improve modeling for climate science. A nice mix of chaos-theory, nonlinear dynamics, and machine learning. I participate in the STERCP project. coordinated from the University of Bergen

SMART-RESEARCH BV is a spin-off company for commercial applications of neural nets, Bayesian networks and other statistical modeling and reasoning techniques. I am executive vice-director. Currently, we do quite well. Some recent projects that we are/were involved in are
  • Bayesian networks for mass disaster victim identification (in collaboration with NFI, partially supported by ICIS (see below)). The system is called Bonaparte. Check its website with live demo (for forensic professionals only, you need to register for the demo).

    Bonaparte is now in use at Netherlands Forensic Institute (NFI), e.g. it has been used/is used
    • to identify victims from the Afriqiyah aircrash in Tripoli, Libya
    • to identify the victims from the MH17 disaster in Ukraine.
      Some coverage of Bonaparte and MH17: and some more (all in Dutch):
    • in the Marianne Vaatstra rape-murder case. The murderer was found in the biggest DNA dragnet search so far.
    • Recently a serial rapist in Utrecht has been caught. He has been found in a familial search that was performed by using Bonaparte, where he turned out to be the brother of somebody in the DNA database NFI press release (Dutch) . In the same way a serial killer has been caught (2017).
    International users: Australia, Vietnam, Interpol. For press releases, items in the news (e.g. NOS journaal item) and awards, see the Bonaparte website.
    Latest news: INTERPOL's I-Familia service for international missing persons has been launched. It uses Bonaparte as cutting-edge matching software. I-Familia

    Big4Data is our brandname under which we do all our other machine learning activities
  • R4Heal: an intelligent healing environment (EFRO)
  • Karmanline: therapeutic games (EFRO)

    Other recent projects,
  • Power network analysis using MCMC
  • Prediction of individual employment potential
  • Diversity prediction. How to predict what costumers will put in their baskets to have a arrangement of items. Think of e.g. a holiday where you want a nice balanced mix of time spent in a village, a festival, a nice walk in nature, beach etc. I developed a method that deals with multi-item probability distributions.
  • Churn prediction (churn is when people change from telecom provider. Telecom companies want predict these risk-costumers, in order to make them a nice offer to stay. So be sure that you are a risk-costumer!!)
  • Wine advice: Neural networks and machine learning applied to learn the computer a taste for wine! Visit www.winewinewine.com, type your evening's dinner and find out what wine to drink!
  • Bovinose: detecting estrus in cow using an electronic nose. See FP7 site for a more detailed description.
  • Enose: cheap detection of tuberculosis using an electronic nose.
  • Fraude detection in on-line transactions.
  • Patterns in big data.
  • Optimization of machine settings.
  • Good old statistical analysis (consultancy based)
  • We are still proud of our system for the prediction of newspaper sales, called JED, which is based on Neural Bayesian technology. It predicts single-copy sales for individual outlets and also errorbars in this prediction. It can deal with missing values and it has the ability that outlets learn from each other. Its main implementation has been at De TELEGRAAF, the largest Dutch newspaper. De Telegraaf saved several 100Keuros per year by more efficient distribution of newspapers to the single-copy sales outlets.
  • Bayesian networks (BayesBuilder) have been applied a.o. for a system for bearing fault analysis Application (SKF), a decision support system for geotechnical engineering (SHELL), and for victim analysis (Bonaparte, see above).

Pevious Scientific projects: SUMO
  • SUMO: climate modeling by Super Modeling. European project in collaboration with KNMI and other partners. 2010-2014. We were abels to demonstrate that quantitative improvements in climate modeling could be gained by connecting different climate models into one supermodel. See EGU BLOG-POST. This project has a follow up:STERCP

Previous scientific projects: ICIS
I have been involved in the ICIS project. See theirwebsite for more information on this huge research project. I did research on
  • multi-agent systems in continuous space and time. In our framework, we can employ techniques from graphical models. With Bart van den Broek (Ph-D student) and Bert Kappen. See our recent UAI papers.
  • Modeling of Bayesian networks, see BNAIC 2005 paper.
  • Application of hybrid MCMC methods in inference with continuous variables.
  • Application of Bayesian networks for forensic research (see above).

Some other previous projects
  • fMRI analysis using ICA (Donders)
  • I worked quite a long time on research and development of Promedas, a medical decision support system based on a large Bayesian network. We developed this system in collaboration with domain experts from University Medical Center Utrecht. The period of mainly research is over, we managed to create systems of about 10000 nodes.
  • We were partner in the Japanese Real World Computing Partnership (1992-2002). Some information can be found on our RWCP - SNN homepage (local version).
  • SIENA Stimulation Initiative for European Neural Applications -Here you can find a number of successfull neural networks applications and some more info. Note: these are old pages, and will not be not updated! In particular email adresses and links will be outdated. However, the examples on the site are still nice illustrations of neural network applications.


We have developed BayesBuilder: a tool to build and evaluate Bayesian networks (downloadable and free for non-commercial purposes).

European projects


BKO (basis kwalificatie onderwijs) certificated by Radboud University

Radboud University:
  • Statistical Machine Learning (masters computer science/ Artificial Intelligence)
  • Inleiding Machine Learning (Bachelor Natuurkunde)

Students: MSc: Jonas Ahrendt (Pedigree reconstruction using MCMC), Christiaan Schoenaker (Bayesian attractor learning), Stefan van den Heuvel (Bayesian Optimization). BA: Joris Bukala (Importance sampling combined with MCMC), Dirren van Vlijmen (Likelihood Ratios with Next Generation Sequencing), Job Mertens (Automatic Pedigree Drawing)

SNN: Foundation for Neural Networks

Vereniging Artificiele Neurale Netwerken