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We are succesful because we choose the right level of abstraction.
Maurits Kaptein is an active researcher in statistics, statistical learning, and research methodology. Over the years his work has focussed on sequential experimentaiton, multi-armed bandit problems, research methodology and causality. In recent years Maurits has been exploring the relationships between various streams in the causal inference literature. Next to his research activities Maurits is also the co-founder and CEO of Scailable; a company that develops an edge AI deployment SDK.
Maurits obtained his MSc. in ecomomic psychology from the University of Tilburg. Subeseuqently, Maurits obtained a PdEng. from the Tehnical Unviersity of Eindhoven in User-System Interaction. Next, Maurits obtained his PhD at Industrial Design at the Technical University of Eindhoven in joint affiliation with Stanford University (Communicaitons). Maurits went on to become a post-doc in quantitatve marketing at Aalto University (Helsinki, Finland), an assistant professor of AI at the Randboud University Nijmegen, and subsequently associate and full professor of statistics at the Unversity of Tilburg. Maurits currently holds a chair in applied causality at the TU/e where he contributes to the research in causality, supervises PhD. students, and actively contributes to TRI-DSA.
Maurits has a strong track record in education and teaching. Maurits has provided BSc., MSc., and PhD. level courses in statistics, research methods, Bayesian methods, and Causality amongst others.
Maurits is the co-author of the statsitics text book "Statistics for Data Scientists" that is used globally in multiple educational programs. The book can be found here: https://link.springer.com/book/10.1007/978-3-030-10531-0.
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Research output: Book/Report › Inaugural/farewell speech › Popular
Research output: Contribution to journal › Article › Academic › peer-review
Research output: Contribution to journal › Article › Academic › peer-review
Research output: Contribution to journal › Article › Academic › peer-review
Research output: Contribution to journal › Article › Academic › peer-review
Kavelaars, X. (Creator), Mulder, J. (Creator) & Kaptein, M. (Creator), Taylor and Francis Ltd., 11 May 2024
DOI: 10.6084/m9.figshare.25801638, https://tandf.figshare.com/articles/dataset/Bayesian_Multivariate_Logistic_Regression_for_Superiority_and_Inferiority_Decision-Making_under_Observable_Treatment_Heterogeneity/25801638 and one more link, https://tandf.figshare.com/articles/dataset/Bayesian_Multivariate_Logistic_Regression_for_Superiority_and_Inferiority_Decision-Making_under_Observable_Treatment_Heterogeneity/25801638/1 (show fewer)
Dataset