Dr. R. de Heide | People Pages: Find employees & contact details

I am an assistant professor in the Statistics group (STAT) in the Department of Applied Mathematics, at the University of Twente. I work on problems and solutions in machine learning and statistics. My research focuses on hypothesis testing, Bayesian learning and best-arm identification problems. Currently I work on my VENI project 'E-values for Multiple Testing’. Central in my work is both bringing a solid mathematical foundation to the topics I work on in different fields, as well as making the theory accessible for less mathematical audiences. I’m also interested in the mathematical and philosophical foundations of Bayesianism, machine learning, statistics and probability theory.

Before coming to the University of Twente, I was an assistant professor at the Vrije Universiteit Amsterdam. Until my family moves north/east, I'm very grateful for the flexibility of the University of Twente to work partly remotely, and for LUXs Data Science in Leiden, and CWI and VU Mathematics in Amsterdam to welcome me as a guest researcher and to let me use their facilities.

I’m committed to DEI and want to contribute to academia being a workplace where everyone feels valued and can be themselves. Be welcome to have a chat with me about it.

Outside of work, some easy topics for conversation are: running, cycling, swimming (guess where this may be going…), hiking, training dogs, raising kids, languages, classical music, and much more!

Please see my website: https://riannedeheide.github.io

Organisations

I work on problems and solutions in machine learning and statistics. My research focuses on hypothesis testing, Bayesian learning and best-arm identification problems. Currently I work on my VENI project 'E-values for Multiple Testing’. Central in my work is both bringing a solid mathematical foundation to the topics I work on in different fields, as well as making the theory accessible for less mathematical audiences. I’m also interested in the mathematical and philosophical foundations of Bayesianism, machine learning, statistics and probability theory.

For more information and my publications, see my website: https://riannedeheide.github.io

Publications

2024

Safe Testing (2024)Journal of the Royal Statistical Society. Series B: Statistical Methodology (E-pub ahead of print/First online). Grünwald, P., de Heide, R. & Koolen, W.https://doi.org/10.1093/jrsssb/qkae011

2023

Attribution-based Explanations that Provide Recourse Cannot be Robust (2023)Journal of machine learning research, 24, 1-37. Fokkema, H., de Heide, R. & van Erven, T.https://www.jmlr.org/papers/volume24/23-0042/23-0042.pdf

2022

On the truth-convergence of open-minded Bayesianism (2022)Review of Symbolic Logic, 15(1), 64-100. Sterkenburg, T. F. & de Heide, R.https://doi.org/10.1017/S1755020321000022Top Two Algorithms Revisited (2022)In 36th Conference on Neural Information Processing Systems, NeurIPS 2022 (Advances in Neural Information Processing Systems; Vol. 35). Neural information processing systems foundation. Jourdan, M., Degenne, R., Baudry, D., de Heide, R. & Kaufmann, E.https://proceedings.neurips.cc/paper_files/paper/2022/hash/ab5f5f22e3e09f4424592ffb06840ab0-Abstract-Conference.html

2021

Optional Stopping with Bayes Factors: A Categorization and Extension of Folklore Results, with an Application to Invariant Situations (2021)Bayesian Analysis, 16(3), 961-989. Hendriksen, A., de Heide, R. & Grünwald, P.https://doi.org/10.1214/20-BA1234Why optional stopping can be a problem for Bayesians (2021)Psychonomic bulletin & review, 28(3), 795-812. de Heide, R. & Grünwald, P. D.https://doi.org/10.3758/s13423-020-01803-xGod, the beautiful and mathematics: A response (2021)HTS Teologiese Studies / Theological Studies, 77(4). Article a6208. Smit, P. B. & Heide, R. d.https://doi.org/10.4102/hts.v77i4.6208Bandits with many optimal arms (2021)In 35th Conference on Neural Information Processing Systems, NeurIPS 2021 (pp. 22457-22469) (Advances in Neural Information Processing Systems; Vol. 34). Neural information processing systems foundation. de Heide, R., Cheshire, J., Menard, P. & Carpentier, A.https://ir.cwi.nl/pub/31354

2020

Fixed-Confidence Guarantees for Bayesian Best-Arm Identification (2020)In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 26-28 August 2020, Online (pp. 1823-1832) (Proceedings of Machine Learning Research; Vol. 108). MLResearchPress. Shang, X., de Heide, R., Kaufmann, E., Ménard, P. & Valko, M.https://proceedings.mlr.press/v108/shang20a.htmlSafe-Bayesian Generalized Linear Regression (2020)In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 26-28 August 2020, Online (pp. 2623-2633) (Proceedings of Machine Learning Research; Vol. 108). MLResearchPress. de Heide, R., Kirichenko, A., Mehta, N. A. & Grünwald, P. D.https://proceedings.mlr.press/v108/heide20a.html

Research profiles

Courses academic year 2024/2025

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2023/2024

There were several interviews with me the last year in (national) newspapers and magazines: NAW (PDF), NRC (PDF, Link), The New Scientist (Picture), and Ad Valvas (Link).