The future of life science research centers on transitioning from descriptive observation to a predictive, closed-loop engine for scientific discovery. Achieving this requires moving beyond standard machine learning models toward a new paradigm of principled AI foundations specifically engineered for the complexities of the biological world. By grounding computational research in the rigor of theoretical computer science, this approach treats biological systems, ranging from molecular structures to complex metabolic pathways, as interconnected relational networks that require robust, physically plausible, and interpretable models to decode.

İsmail İlkan Ceylan

Dr. İsmail İlkan Ceylan is an Associate Professor at TU Wien and an Adjunct Principal Investigator at AITHYRA. He holds an academic affiliation with the University of Oxford, where he previously served as an Assistant Professor in Computer Science. He earned his Ph.D. from TU Dresden in 2017, receiving the prestigious E.W. Beth Dissertation Prize. Dr. Ceylan specializes in graph machine learning, utilizing techniques from both machine learning (e.g., Graph Neural Networks, geometric deep learning) and theoretical computer science (e.g., logic, graph theory). His long-term goal is to build reliable, robust systems capable of reasoning over complex relational structures, especially at the intersection of AI and life sciences. He is dedicated to developing trustworthy, interpretable, and scalable geometric deep learning methods to accelerate breakthroughs in biomedicine and scientific discovery.

Matthias Lanzinger

Matthias Lanzinger is an Assistant Professor in the Research Unit Databases and Artificial Intelligence at TU Wien Informatics. His research lies at the intersection of logic, algorithms, and machine learning, exploring how structural insights can make intelligent systems more efficient, transparent, and reliable. Before joining TU Wien, Matthias was a Senior Research Associate at the University of Oxford and a Stipendiary Fellow at Jesus College, University of Oxford.

Activities

Forthcoming