Abstract

One of the goals for FCAI is to make 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.

Machine Learning Coffee seminars are weekly seminars organized by Finnish Center for Artificial Intelligence (FCAI) and are held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held weekly on Mondays at 9:00-10:00. The location alternates between Aalto University and the University of Helsinki. At Aalto University, talks will be held in Konemiehentie 2, seminar room T6 and at the University of Helsinki in Kumpula, seminar room Exactum D122 (Gustaf Hällströmin katu 2b), unless otherwise noted. Talks will begin at 9:15 and coffee will be served from 9:00.