Kathrin Hellmuth

Kathrin Hellmuth is a PostDoc at the California Institute of Technology (Caltech), affiliated with the Computing and Mathematical Sciences (CMS) division under the supervision of Franca Hoffmann. She holds a PhD in Mathematics from Julius-Maximilians-Universität Würzburg, Germany, where she was supervised by Prof. Dr. Christian Klingenberg. Her research focuses on inverse problems in partial differential equations (PDEs), mathematical modeling of chemotaxis, and numerical methods integrating machine learning. She also explores applications in financial mathematics, such as solving the Black-Scholes equation with uncertain volatility.

Education highlights include a PhD (April 2025) with a thesis on qualitative experimental design for PDE parameter identification, an M.Sc. in Mathematics (April 2020) from University of Würzburg (with an Erasmus+ semester at the University of Padua, Italy), and a B.Sc. in Mathematics (October 2017).

Her work bridges theoretical analysis and applied mathematics, with contributions to kinetic chemotaxis models, Bayesian inverse problems, and multiscale convergence techniques. She has published in journals like SIAM Journal on Applied Mathematics and has participated in international conferences such as HYP 2022. Her teaching experience includes leading exercise classes in partial differential equations, linear algebra, and mathematics for machine learning at the University of Würzburg.