Research/Areas of Interest
numerical optimization; scientific computing; scientific machine learning; numerical analysis; inverse problems; parallel computing
Education
- Doctor of Philosophy, University of Luebeck, DEU
Biography
My research develops principled and scalable algorithms at the intersection of machine learning, optimization, and scientific computing, with the goal of integrating data and simulation to enable data-driven scientific discovery. I am particularly interested in approaches that combine the interpretability and predictive power of mechanistic models with the flexibility of modern learning methods, and in the numerical foundations that make such approaches reliable: stability, structure preservation, scalability, and rigorous quantification of uncertainty.