I am a PhD student in Machine Learning and Artificial Intelligence at the Empirical Inference Department of the Max Planck Institute for Intelligent Systems, where I am advised by Bernhard Schölkopf. I hold a Master’s degree in mathematics from the University of Cambridge and a Bachelor’s degree in physics from the University of Frankfurt.
Currently my research interest is focussed on distributionally robust optimization and method of moments estimation for causal inference. Previously I have worked on epidemiological modelling, computational image analysis and computer-generated holography.
- (May 2023) Our paper Estimation Beyond Data Reweighting: Kernel Method of Moments got accepted at ICML 2023
- (May 2023) Gave a talk on Distributionally Robust Machine Learning with Kernel Machines at SIAM Optimization 23 in Seattle
- (Feb 2023) Our paper Compact holographic sound fields enable rapid one-step assembly of matter in 3D got published at Science Advances
- (Dec 2022) Gave an invited talk on Distributionally Robust Machine Learning via Conditional Moment Restrictions at the Weierstrass Institute for Applied Analysis and Stochastics, Berlin
- (July 2022) Our paper Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee got accepted at CDC2022
- (May 2022) Our paper Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions got accepted at ICML 2022