Curriculum Vitae

Full name and title for official stuff: Dr. T.A.L. van Erven

Research Interests

In my research group we design mathematically well-founded machine learning methods for online convex optimization that work well out of the box, without any manual fine-tuning. We do this by identifying statistically ‘easy’ situations (e.g. low noise, small norm optimal parameters, etc.) in which it is possible to learn more efficiently, with less data. We then construct adaptive methods that exploit these easy cases when present, but automatically fall back to slower robust learning strategies when there is no easy structure. This is technically challenging, because it typically requires automatic hyper-parameter tuning, for which we develop new approaches.

In addition, we have recently started on a formal mathematical analysis of explainability methods, which explain the black-box decisions made by machine learning systems to a user. In the past I have also worked on topics in information theory, model selection for Bayesian statistics, and PAC-Bayesian concentration inequalities.


  • Senior Program Committee member for COLT 2015-2023
  • Senior Program Committee member for ALT 2022
  • Area Chair for NeurIPS 2018, 2019, 2020
  • Reviewer for COLT, NeurIPS, ICML, IEEE Transactions on Information Theory, the Journal of Machine Learning Research, Machine Learning, BNAIC/BeneLearn, etc.
  • COLT 2023 Open problems chair


2023 - present Member of the board of directors of the association that organizes COLT
2023 - present Co-chair for the AI & Mathematics initiative
2018 - present Co-organizing the thematic seminar with current theme machine learning
2017 - 2022 Organizing the Netherlands N(eur)IPS debriefing
2019 Co-organizer of the Young European Statisticians workshop on the Theoretical Foundations of Deep Learning
2016 - 2017 Local arrangements chair for the COLT 2017 machine learning conference in Amsterdam, together with Wouter Koolen
2016 - present Running the machine learning Nederland mailing list for machine learning in the Netherlands
2015 Organizer of the workshop Learning Faster from Easy Data II at NIPS 2015.
2008 Co-organizer of the workshop Recent Breakthroughs in MDL Learning (ICML/UAI/COLT, Helsinki, Finland)


Grants and Awards

2019 VIDI grant by the Dutch Research Council
2016 TOP grant, compartment 2 by the Dutch Research Council
2014 NIPS 2014 outstanding reviewer award
2011 Awarded a Rubicon grant by the Dutch Research Council to do a two-year postdoc in Paris with Pascal Massart
2009 A preprint of the paper Catching up faster by switching sooner won second prize in the student paper competition of the Risk Analysis Section of the American Statistical Association.
≤ 2006 Obtained both bachelor's and master's degree cum laude (with honors)


Here is a list of some of the classes I have taught.

Positions and Studies

2020 - present Associate professor in the Korteweg-de Vries Institute for Mathematics at the University of Amsterdam
2014 - 2020 Assistant professor in the statistics group at Leiden University
2012 - 2014 Postdoc at Université Paris-Sud in France in the group of Pascal Massart, and member of the INRIA team SELECT
2011 - 2012 Postdoc at VU University with Aad van der Vaart
2010 - 2011 Postdoc at CWI with Peter Grünwald
2006 - 2010 PhD student at CWI under supervision of Peter Grünwald
2005 Trainee at CWI
2000 - 2006 Studied Artificial Intelligence at the University of Amsterdam