Dr. A.J. Schmidt Hieber

Biography

Johannes Schmidt-Hieber is a professor at the University of Twente, specializing in statistics and mathematics. His research focuses on Gaussian process bounds, Kriging, linear models, regularization parameters, and Bayesian estimators. He is known for his work on nonparametric regression, particularly using deep neural networks, and Bayesian analysis in the context of various statistical models. Throughout his academic career, he has contributed to numerous preprints and articles in reputable journals, including the Annals of Statistics and Electronic Journal Statistics, tackling complex problems such as posterior consistency and adaptive estimation in high-dimensional settings. Schmidt-Hieber actively collaborates with fellow researchers, contributing to significant advancements in statistical theory and its applications, especially in quantitative nanoscopy and Bayesian variance estimation. He has a strong educational background with a doctorate and has published extensively in the field, reflecting his commitment to advancing statistical knowledge.

Research Interests

Courses