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Thomas is an Assistant Professor at the Department of Epidemiology and Data Science of Amsterdam University Medical Centers. His research focuses on methodology for personalized medicine and prevention. In particular, he has applied and developed statistical models and machine learning tools to estimate treatment effect heterogeneity from observational data, optimal treatment regimens, and models for personalized (cancer) screening. A special interest of his are techniques for Causal Inference, on which he coordinates a yearly Winter School class in the Amsterdam UMC Masters in Epidemiology program.
Thomas holds a PhD degree in survey methodology and statistics. During his PhD and post-doctoral experience, he has worked at statistics departments of Utrecht University and the Dutch national bureau of statistics, Statistics Netherlands. In this time, he focused on the estimation and adjustment of errors in statistics based on population surveys, such as errors due to non-response (missing data) and measurement error. For his PhD thesis he received the Van Zwet award (2015) by the Dutch Society for Statistics and Operations Research.