Keynote Lecture

Yvan Saeys

Ghent University

AI’s Odyssey in cytometry wonderland: challenges and prospects of emerging foundation models for single-cell analysis

Recent advances in artificial intelligence are quickly finding their way in scientific research, and applications of large language models, foundation models and other large-scale modelling efforts are leaving scientists bedazzled, wondering whether they should embrace these novel advances, or remain more cautious and rely on simpler models they can understand more easily. In this talk I will first introduce some basic building blocks of these novel AI models, and show some examples of how they can be useful to construct next-generation representations of single-cell data. Subsequently I will give a high-level overview of what might be future applications of all these models in the cytometry field, and which novel questions and challenges they raise.

Biosketch

Yvan Saeys obtained his PhD in computer science from Ghent University. After spending time abroad at the University of the Basque Country (Spain) and the University of Lyon (France) he returned to Belgium and established the Data Mining and Modeling for Biomedicine (DAMBI) group at the VIB Center for Inflammation Research (IRC) in Gent. As of 2015, he is a professor at Ghent University and a principal investigator (group leader) at VIB, where he is heading an interdisciplinary research team of 21 people, consisting of mathematicians, computer scientists, engineers and bioinformaticians.  

The Saeys lab studies the design and application of novel data mining and machine learning techniques for high-dimensional single-cell data, including automation of quality control, preprocessing and interpretation of cymetry data.   At the methodological level, the lab studies the robustness and interpretability of machine learning models and their applications in biomedicine.