News

• I am terribly proud of my postdoc Osman Mian for receiving the AAAI Outstanding Paper Award 2026 for his work on “Causal Structure Learning for Dynamical Systems with Theoretical Score Analysis“.
• Our project FLIP-IT for federated learning in a network of general practitioners started in January, 2026. The project is funded by KI.NRW (EFRE) with the goal to build a trustworthy and secure federated learning infrastructure in GP practices. With this, we will train early warning models for chronic kidney disease to protect patients from kidney failure and dialysis.
• My PhD student Ting Han, my colleagues Linara Adilova, Henning Petzka, Jens Kleesiek, and I published a paper on “Flatness is Necessary, Neural Collapse is Not: Rethinking Generalization via Grokking” at NeurIPS 2025 (A*, top 7%)
• My colleagues Linara Adilova, Bruno Casella, Samuele Fonio, Mirko Polato and I are organizing the Workshop on Federated Learning in Critical Applications at AAAI, 2026 in Singapore.
• I have been appointed Associate Professor for Machine Learning and Artificial Intelligence at TU Dortmund University and joined the Lamarr Institute for Machine Learning and Artificial Intelligence. I will continue my research on deep learning theory, causality, and trustworthy federated learning, with a strong focus on medical and high-stakes applications.
• We are presenting two papers at AAAI 2025 (A*, top 7%): Amr Abourayya, Jens Kleesiek, Kanishka Rao, Erman Ayday, Bharat Rao, Geoffrey I. Webb, and I published a paper on “Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning” and Sebastian Dalleiger, Jilles Vreeken, and I published a paper on “Federated Binary Matrix Factorization using Proximal Optimization“.

We are also part of the Cancer Research Center Cologne Essen (CCCE). In addition, Jens Kleesiek is PI in the German Cancer Consortium (DKTK) and at the Helmholtz Information & Data Science School for Health. There is a close cooperation with the German Cancer Center (DKFZ) for developing the Joint Imaging Platform (JIP) for distributed data analysis and federated learning.