Regular biography
Harald Oberhauser is a Research Fellow at the Mathematical Institute, University of Oxford, within the Department of Mathematics. His research interests focus on mathematics that enables understanding, modeling, and inference about systems influenced by randomness. He is affiliated with the Machine Learning and Data Science research group and the Stochastic Analysis group. Oberhauser has contributed to areas such as stochastic differential equations, financial engineering, and nonlinear filtering. His work includes publications on splitting methods for SPDEs, rough path stability, and the functional Itō formula. He is also involved in teaching courses on stochastic differential equations and information theory. He is seeking DPhil (PhD) students and encourages interested candidates to contact him.
Scholar-generated biography
Harald Oberhauser is a researcher at the Mathematical Institute, University of Oxford, focusing on mathematical finance, stochastic processes, and data analysis. His work explores the application of rough paths and signatures to model and analyze complex stochastic systems. He investigates methods for robust filtering, nonlinear stochastic partial differential equations, and topological data analysis. Oberhauser's research also includes Bayesian learning, kernel methods, and financial time series analysis. His publications address challenges in high-frequency data, correlated noise, and adaptive inference. His contributions span both theoretical and applied aspects of stochastic calculus and machine learning.