Sjoerd Dirksen

Sjoerd Dirksen is a Professor of Mathematics for Data Sciences at Utrecht University since May 2025, having previously served as an Associate Professor for Applied Mathematics (2019-2025) and Junior Professor at RWTH Aachen University (2014-2019). He is affiliated with the Mathematical Institute within the Faculty of Science at Utrecht University, where his office is located in the Hans Freudenthal Building.

His research interests focus on high-dimensional probability theory and its applications in data science, machine learning, and signal processing. Specifically, he investigates randomized data dimension reduction methods using structured random matrices, theory for deep learning including random neural networks, high-dimensional covariance estimation for wireless communication systems, and statistical postprocessing of weather forecasts in collaboration with the Royal Netherlands Meteorological Institute (KNMI). Previously, he worked on compressed sensing, sharp estimates for stochastic processes in Banach spaces, and noncommutative analysis.

Analysis of his recent publications (2018-2024) reveals a strong focus on quantization effects in high-dimensional data processing, particularly one-bit compressed sensing and covariance estimation under coarse quantization. His work bridges theoretical mathematics with practical applications in signal processing, wireless communications, and meteorological forecasting, demonstrating a consistent trajectory from foundational mathematical research to applied data science problems.

Dirksen's academic career shows progression from postdoctoral work at the Hausdorff Center for Mathematics in Bonn to independent research positions. His publication record demonstrates significant contributions to the mathematics of data science, with papers appearing in top journals across mathematics, statistics, and signal processing. His research combines deep theoretical insights with practical applications, particularly in the areas of dimensionality reduction and high-dimensional statistics.