Peter Grünwald heads the machine learning group at CWI in Amsterdam, the Netherlands. He is also full professor of Statistical Learning at the mathematical institute of Leiden University.  Currently the President of the Association for Computational Learning, the organization running COLT, the world’s prime annual conference on machine learning theory, he was co-program chair of COLT in 2015 and also chaired UAI – another top ML conference – in 2010/2011. Apart from publishing at ML venues like NIPS, COLT and UAI, he also regularly contributes to statistics journals such as the Annals of Statistics. He is the author of the book The Minimum Description Length Principle, (MIT Press, 2007; see here for an up-to-date (2020), much shorter introduction), which has become the standard reference for the MDL approach to learning. In 2010 he was co-awarded the Van Dantzig prize, the highest Dutch award in statistics and operations research. He received NWO VIDI (2005), VICI (2010) and TOP-1 (2016) grants.

Peter’s research currently focuses on Safety and Luckiness. The basic idea is to make sure that inference from data is done in – indeed – a safer way!  Recently, most emphasis has been on testing that is safe under optional stopping/continuation; here is a general CWI project page with info on applying these ideas to meta-analysis. Recent publications/preprints in the general safety and luckiness direction:

Note: at the ML group at CWI we do not offer internships for master’s and bachelor’s students from outside of the Netherlands. We only have an international internship program for Ph.D. students that are in their second or later years (I get so many requests for internships these days that I cannot respond to each request individually).