Track chair
Sjoerd Dirksen, Utrecht University
Track committee
Tim van Erven, University of Amsterdam
Silke Glas, University of Twente
Mihaela Mitici, Utrecht University
Track description
Artificial Intelligence (AI) is transforming various areas of society, such as healthcare, finance, robotics, and logistics, and is increasingly integrated as a tool in many scientific disciplines. At the same time, major challenges need to be addressed to ensure that AI methods become energy-efficient, robust, explainable, privacy-preserving, and fair. To develop a new generation of AI methods with these properties, a combined effort from mathematics, computer science, and other fundamental sciences is required to solve fundamental problems at the heart of AI.
This track provides a platform to connect Dutch research on fundamental aspects of AI. We welcome abstract submissions on recent advances, which may be based on published work, as well as abstracts on new challenges that require a combined effort from different disciplines. Possible topics include, but are not limited to, novel methods in deep learning, uncertainty quantification in deep learning, reinforcement learning, machine learning in optimization and operations research, machine learning for imaging, manifold learning, explainability methods, fairness, privacy, causal inference, and anomaly detection.
This track is an initiative of the Dutch NWO AI and Mathematics network (AIM, aimath.nl).
