Leader of the group Machine Learning: Peter Grünwald.

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.



Sander Bohté, professor by special appointment of Cognitive Neurobiology at UvA

Sander Bohté, professor by special appointment of Cognitive Neurobiology at UvA

Sander Bohté (1974) has been named professor by special appointment of Cognitive Neurobiology, specialising in Computational Neuroscience, at the University of Amsterdam (UvA)'s Faculty of Science. The chair was designated on behalf of the Science Plus Foundation (Stichting Bèta Plus). He will be combining his professorship with his position as senior researcher at CWI's Machine Learning group.

Sander Bohté, professor by special appointment of Cognitive Neurobiology at UvA - Read More…

Current events

ML Seminar Audra McMillan (Boston University/Northeastern University)

  • 2020-03-26T11:00:00+01:00
  • 2020-03-26T12:00:00+01:00
March 26 Thursday

Start: 2020-03-26 11:00:00+01:00 End: 2020-03-26 12:00:00+01:00


This is a joint CWI Security and Machine Learning seminar.

Private Hypothesis Testing via Robustness

Audra McMillan (Boston University/Northeastern University)

Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. In this talk, we will discuss the role that robustness plays in designing good differentially private hypothesis tests. We’ll focus on two foundational problems: simple hypothesis testing and identity testing in high dimensions. Both problems are challenging to solve privately, for slightly different reasons. We’ll see how improving the robustness of non-private tests allows us to design private algorithms that adapt to the specific instance they are given. We’ll also discuss the role that dimensionality plays in private hypothesis testing in high dimensions.





Associated Members



Current projects with external funding

  • Efficient Deep Learning Platforms (eDLP)
  • Enabling Personalized Interventions (EPI)
  • Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)
  • Spiking Neural Networks research program

Related partners

  • Philips
  • KPMG
  • SURFsara B.V.
  • Technische Universiteit Eindhoven
  • Universiteit Twente
  • Universiteit van Amsterdam
  • Vrije Universiteit Amsterdam