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Program at a Glance (Tentative)
Program at a Glance (Tentative)
Monday, February 2
Monday, February 2
09.00 Registration opens
09.30 - 11.30 Pre-Workshop Tutorial on Uncertainty
11:30: Coffee Break
11.45 - 12.00 Welcome & Opening
12.00 - 13.00 Invited Talk
13:00-14:00 Lunch Break
14.00 - 15.30 Poster session
15.30 - 16.00 Coffee Break
16.00 - 17:30 Presentations
Conference dinner
Tuesday, February 3
Tuesday, February 3
09.00 - 10.00 Invited Talk
10.00 - 11.30 Poster session and coffee
11.30 - 13.00 Presentations
13.00 - 14.00 Lunch Break
14.00 - 14.30 Invited Talk
14.30 - 16.00 Presentations
16.00 - 16.30 Coffee Break
16.30 - 17.30 Presentations
Social mixer
Wednesday, February 4
Wednesday, February 4
09.00 - 10.30 Presentations
10.30 - 11.00 Coffee Break
11.00 - 13.00 Presentations
13.00 - 14.00 Lunch Break
14.00 - 14.15 Closing
14.15 - 17.00 Presentations & project meetings
Optional program
Keynote speakers
Keynote speakers
Prof. Willem Waegeman is a Professor at Ghent University in the Department of Data Analysis and Mathematical Modelling, within the Faculty of Bioscience Engineering. He is leading the BioML research unit (www.bioml.ugent.be). His research focuses on machine learning and data science, particularly uncertainty quantification, multi-target and other complex prediction problems, deep learning and applications in “omics” data analysis.
Ilja Kuzborskij is a research scientist at Google DeepMind in London, focusing on the design and analysis of learning algorithms. His work concentrates on theoretical aspects of machine learning, including the theory of generalization, methods for uncertainty estimation, and the use of concentration inequalities.
Invited speaker
Invited speaker
Arun Kumar Singh is an Associate Professor at Institute of Technology, University of Tartu. Earlier he has been a postdoc at Ben Gurion University, Israel, Nanyang Technological University, Singapore and Tampere University of Technology, Finland. His research interests lie in the algorithmic foundation of robotics in the context of motion planning and control and machine learning. The current research threads include optimization for robotics and learning, multi-agent navigation, autonomous driving, shared control, motion planning under uncertainty.
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