Machine learning for spatio-temporal datasets + SAILS data observatory

Spatio-temporal datasets (e.g., GPS trajectories, Earth observations) are ubiquitous. Algorithms for effective and automated processing of such data are relevant from various applications, from crowd movement analysis to environmental modelling. These algorithms need to be designed considering the fundamental aspects of the underlying spatio-temporal processes (e.g., the existence of spatial and temporal correlations) and be robust against various ubiquitous data imperfection issues. In this talk, I will introduce the field of spatio-temporal data mining and talk about crucial open research challenges for making use of such data.

I would also like to discuss the vision of creating a “data observatory” to address various important research challenges in multi-disciplinary research. The data observatory aims to bring together datasets (the observations), AI algorithms (the tools), and expertise (the humans) in a well-equipped setting that facilitates a collaborative investigation.

Meeting link

Please contact Chris Flinterman for the meeting link.