Hi I’m Heiner, welcome to my scientific website!
I am a PhD student working on 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.
My current research interest is focussed on developing statistical methods to learn from conditional moment restrictions, a problem formulation which arises in many fields ranging from causal inference (e.g. instrumental variable regression) over economics to reinforcement learning and generally robust machine learning. In this context I am mostly working on empirical likelihood methods and distributionally robust optimization. Previously I have worked on epidemiological modelling, computational image analysis and computer-generated holography. Please visit my research page for details.
You can also find me on Github, LinkedIn, Google Scholar.
- (01/07/2022) Our paper Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions got accepted at ICML 2022
- (25/04/2022) Our new preprint Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee is now on arxiv
- (01/04/2022) Our paper Listening to bluetooth beacons for epidemic risk mitigation got published at Scientific Reports