The MosAIK team organizes a seminar open to all the laboratory on Tuesday, 8 July at 11am in room A008.

Willem Waegeman, associate professor at Ghent University, will give a presentation entitled Making AI systems more trustworthy through uncertainty disentanglement.

Abstract:
Given the increasing use of machine learning (ML) models for decisions that directly affect humans, it is essential that these models not only provide accurate predictions but also offer a credible representation of their uncertainty. Recent advances have led to probabilistic models capable of disentangling two types of uncertainty: aleatoric and epistemic. Aleatoric uncertainty is inherent to the data and cannot be eliminated, while epistemic uncertainty is related to the ML model and can be reduced with better modeling approaches or more data. In this talk I will elaborate on the opportunities and limitations of uncertainty disentanglement in explaining why an ML model fails to deliver accurate predictions. Furthermore, I will discuss several use cases that demonstrate the potential of uncertainty disentanglement for biotechnology applications.

Bio:

Willem Waegeman is an associate professor at Ghent University, and group leader of the BIOML group of the Department of Data Analysis and Mathematical Modelling. His main research interests are machine learning and bioinformatics. Specific interests include uncertainty quantification and complex prediction problems, such as multi-target and structured prediction problems. He is an author of more than 100 peer-reviewed papers in journals and conferences, and his work has won several prizes. In recent years he has served on the program committees of leading conferences in AI (ICML, Neurips, ECML/PKDD, ICLR, UAI, AISTATS, IJCAI, etc.).