I am a Postdoc Fellow affiliated with the ETH AI Center and the Medical Data Science group at ETH Zürich. My research focuses on causality, information theory, and multimodal learning to approach modern challenges in the medical and biological domain. I obtained my Master’s degree in Bioinformatics in 2016 and completed my PhD in Computer Science in 2021 at Saarland University. During my PhD, I was part of the Exploratory Data Analysis group affiliated with the CISPA Helmholtz Center for Information Security and the Max Planck Institute for Informatics.

For Students: If you are looking for a thesis/semester project, feel free to contact me. Open projects in the medical domain include time series classification & forecasting of blood glucose from multiple data sources or representation learning from multimodal data (see the projects I talk about here). Additionally, we can discuss projects related to mutual information estimation (see SDM'21, SDM'22), or causal discovery under weak assumptions (see, e.g. AAAI'21, UAI'21).

News

• (May 2022) Our paper on cause-effect inference in the presence of heteroscedastic noise got accepted to ICML'22.
• (Apr. 2022) A portrait article about me and my research got featured in ETH news.
• (Feb. 2022) AISTATS'22 Top 10% Reviewer Award
• (Dec. 2021) Our paper on kNN-based Mutual Information estimation via Geodesic Forests (preprint) got accepted at SDM'22.
• (Dec. 2021) Two papers (one, two) were accepted the AAAI-22 Workshop ITCI'22.
• (Dec. 2021) I will co-teach the course AI Center Projects in Machine Learning Research in the upcoming summer semester.
• (Sep. 2021) I started as a Post-Doc Fellow at the ETH AI Center.

Activities

Reviewing for Conferences & Journals

• Reviewer for the International Conference on Machine Learning (ICML'22)
• Reviewer for the International Conference on Artificial Intelligence and Statistics (AISTATS'21,22 [Top 10% Reviewer Award])
• PC Member for the SIAM International Conference on Data Mining (SDM'21)
• PC Member for the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'21)
• PC Member for the AAAI-22 Workshop on Information-Theoretic Methods for Causal Inference and Discovery (ITCI'22)
• Reviewer for the IEEE International Symposium on Information Theory (ISIT'19,20)
• Reviewed for IEEE Access in 2020

Invited Talks

• Data Mining reading group, TU Eindhoven (virtual), 2020
• CWI Machine Learning Seminar, CWI Amsterdam, 2019
• Explanatory Data Analysis group, Leiden University, 2019