Rianne de Heide
Assistant professor, Department of Mathematics
I work on problems and solutions in machine learning and statistics. My research focuses on sequential learning, and in particular on (sequential) hypothesis testing, Bayesian learning and best-arm identification problems. I am interested in exploring the limitations of existing methods under misspecification of the model or the data collecting process, and in devising new, robust methods for these settings. I’m also interested in the mathematical and philosophical foundations of Bayesianism, machine learning, statistics and probability theory.