Abstract: One of the goals for FCAI is to make the development of probabilistic AI solutions easier and faster, by combining fundamental research on modeling principles with open-source tools that are being used by the industry and academia. This talk provides an overview to the activities related to this, and in particular drills into recent research on how the prior predictive distribution can be used for eliciting expert knowledge and for automatic parts of the modeling process.

Bio: Arto Klami is an assistant professor of computer science at University of Helsinki. He leads the Multi-source Probabilistic Inference group and is coordinating the FCAI Highlight on Easy and privacy-preserving modeling tools. He received his PhD from Aalto University in 2008 and worked as Academy Research Fellow in 2013-2019. He has published more than 60 scientific articles and his main research area is statistical machine learning, with contributions for example in data integration and approximate inference. He has worked on wide range of applications from computational neuroscience to modeling human activity, and is currently focusing on AI-driven ultrasonic cleaning and hyperspectral imaging.

Speaker: Arto Klami

Affiliation: Assistant Professor, Department of Computer Science, University of Helsinki

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki