My research focuses on integrating causal inference and reasoning with machine learning algorithms. I have developed models that merge causal modeling with infection dynamics simulations, specifically to evaluate vaccination strategies against COVID-19. Another area of my research is causal representation learning, which involves extracting causal relationships from multi-environment data or distilling complex, high-dimensional scientific simulations into interpretable causal models
My academic background is in Physics and Applied Mathematics, with studies completed at Heidelberg, Oxford, and Paris. Subsequently, I joined Zalando as an applied scientist, focusing on developing machine learning solutions for algorithmic pricing, including high-dimensional time series forecasting and optimization.
I am open to collaborations and mentorships, especially for internships, Bachelor's, or Master's theses. If you're keen on working together, please reach out via email with your CV, transcripts, and a brief overview of your research interests.