Ruud Wetzels works as a professor of Data Science, researching innovative analytics methods, artificial intelligence and machine learning. In addition to theoretical research, he also deals with the question of how these techniques can be optimally used in companies.

Ruud obtained his doctorate at the University of Amsterdam with a dissertation on Bayesian statistics and reinforcement learning and has published works on both fields in academic journals, in relation to which he has been quoted more than 2,000 times. He has also worked as a post-doctoral researcher on machine learning models for biomedical (brain) applications, worked for the European Commission in Dublin and is presently Senior Manager at PwC where he helps companies create value from data.

Secondary positions

Ruud Wetzels is Senior Manager Data Analytics - Advisory at PwC and Advisory Board Member at JASP, a platform-independent software program.

Interests

Ruud enjoys reading, music, and sports.

Most relevant publications

  • Wagenmakers, E.-J., Wetzels, R., Borsboom, D., & van der Maas, H. L. J. (2011). Why psychologists must change the way they analyze their data: The case of psi: Comment on Bem (2011). Journal of Personality and Social Psychology, 100(3), 426–432. https://doi.org/10.1037/a0022790
  • Wetzels, R. (2012, July 14). A default Bayesian hypothesis test for correlations and partial correlations. Psychonomic Bulletin & Review. https://doi.org/10.3758/s13423-012-0295-x
  • Wetzels, R., Vanderkerckhove, J., Tuerlinckx, F., & Wagenmakers, E.-J. (2009, January 13). Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task. ScienceDirect. https://doi.org/10.1016/j.jmp.2008.12.001
  • Wetzels, R., Grasman, R., & Wagenmakers, E. J. (2010, April 1). An encompassing prior generalization of the Savage-Dickey density ratio. ScienceDirect. https://doi.org/10.1016/j.csda.2010.03.016
  • Wetzels, R. (2012, July 14). A default Bayesian hypothesis test for correlations and partial correlations. Psychonomic Bulletin & Review. https://doi.org/10.1080/00031305.2012.695956