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Sjoerd Dirksen is a professor at Utrecht University specializing in high-dimensional probability theory and its applications in mathematics, data science, machine learning, and signal processing. His current research interests include randomized data dimension reduction methods, utilizing structured random matrices and random hyperplane tessellations. He is investigating the theoretical aspects of deep learning, especially properties of initialization in random neural networks and their robustness and memorization capacity. Dirksen is also focused on high-dimensional covariance estimation, which is particularly motivated by wireless communication systems and massive MIMO antenna systems. His expertise extends to phase retrieval in low-dose settings and statistical postprocessing of weather forecasts, collaborating with the Royal Netherlands Meteorological Institute (KNMI). Previously, he has worked on compressed sensing and derived significant reconstruction results in randomized measurement models. His work includes sharp estimates in martingales, stochastic integrals, and convolutions in the context of Banach spaces, and he has made contributions to noncommutative analysis involving von Neumann algebras. His research incorporates complex areas, reflecting a broad application of probability theory in mathematical research.
Department of Psychology