Leader of the group Machine Learning: Peter Grunwald.

Our research group focuses on how computer programs can learn from and understand data, and then make useful predictions based on it. These algorithms integrate insights from various fields, including statistics, artificial intelligence and neuroscience.  

 Machine-learning applications are increasingly part of every aspect of life, from speech recognition on cell phones to illness prediction in healthcare. One common problem is extremely polluted data, for which no single model can provide adequate explanations. At CWI we address this issue with statistical machine learning based on combining predictions from different models and experts in order to achieve reliable conclusions.

We also study how networks of neurons in the brain process information, and how modern deep-learning methods can benefit from neuroscience. We develop novel neural networks, like Deep Adaptive Spiking Neural Networks, and also theoretical models of neural learning and information processing in biology. Applications of our work range from low-energy consumption neural machine learning to neuroprosthetics, to increased insight into the question of how the brain works.


Current events

CWI Lectures on Machine Learning

  • 2017-11-23T12:00:00+01:00
  • 2017-11-23T18:00:00+01:00
November 23 Thursday

Start: 2017-11-23 12:00:00+01:00 End: 2017-11-23 18:00:00+01:00

Turingroom at Congress Centre Amsterdam Science Park

Registration is now closed as we have reached our full capacity for the conference.

You can register for the waiting list and about 2 weeks prior to the event, we will let you know if we have been able to find you a seat.


This year, the CWI Lectures will be held on Thursday 23 November. The theme will be Machine Learning and the afternoon is organized by CWI's newly established Machine Learning group. 

The speakers are all world leading machine learning researchers:

- Neil Lawrence leads Amazon Research Cambridge where he is a Director of Machine Learning. He is on leave of absence from the University of Sheffield where he is a Professor in Computational Biology and Machine Learning in the the Department of Computer Science.
Neil’s main research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application. He has a particular focus on applications in personalized health and computational biology, but happily dabbles in other areas such as speech, vision and graphics.


- Suchi Saria is professor at Johns Hopkins University. Even though she only obtained her Ph.D. (at Stanford) in 2011, her work has already attracted considerable attraction and honours. For example, in 2016 she has been selected as 'one of Popular Science's Brilliant 10'. She has also been named one of AI's "ten to watch" by IEEE in 2014. She is, among many other things, known for successful applications of state-of-the-art theory in machine learning and statistics within the health domain. 


- Max Welling is a research chair in Machine Learning at the University of Amsterdam.  He has secondary appointments at the Canadian Institute for Advanced Research (CIFAR). He is co-founder of “Scyfer BV” a university spin-off in deep learning, recently acquired by Qualcomm. Welling’s machine learning research labs are AMLAB, Qualcomm QUVA Lab and Bosch Delta Lab".



- Csaba Szepesvári is a research scientist at Google Deepmind and a Professor at the Department of Computing Science of the University of Alberta. He is best known for the UCT algorithm, which led to a leap in the performance of planning and search algorithms in many domains, in particular in computer Go. His research interests include reinforcement learning, statistical learning theory and online learning.

For titles and abstracts of the presentations, please click here.

12.00-12.45 lunch
12.45-13.00 Welcome by Jos Baeten (General Director CWI) & Peter Grünwald (leader of the CWI Machine Learning Group)
13.00-13.45 Neil Lawrence
13.45-14.30 Suchi Saria
14.30-15.15 break
15.15-16.00 Max Welling
16.00-16.45 Csaba Szepesvári
16.45-16.50 Closing
16.50-18.00 drinks

Registration is closed




Associated Members



Current projects with external funding

  • Machine Learning at the Intrinsic Task Pace
  • Deep Spiking Vision: Better, Faster, Cheaper (DEVIS)
  • Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)