Fabio Ramos (Uni. of Sydney): Learning and Planning in Spatial-Temporal Data

Abstract: Modern sensors provide immense amounts of information that need to be efficiently integrated into probabilistic models representing the environment autonomous systems operate in. In this talk I will show statistical machine learning methods for spatial and spatial-temporal data that are able to fuse information from heterogeneous sources, scaling gracefully to very large datasets. I will demonstrate how Bayesian reasoning and the principle of modelling uncertainty can be used to mitigate risks in decision making, for motion planning with indoor robots, to continental-scale natural resource exploration.

Bio: Fabio Ramos is an Associate Professor in machine learning and robotics at the School of Information Technologies, University of Sydney, and co-Director of the Centre for Translational Data Science. He received the B.Sc. and the M.Sc. degrees in Mechatronics Engineering at University of Sao Paulo, Brazil, in 2001 and 2003 respectively, and the Ph.D. degree at University of Sydney, Australia, in 2008. He has over 130 peer-reviewed publications and received best paper awards at ECML’18, IROS’05, ACRA’07, and Best Paper Finalist at RSS’17. His research focuses on statistical machine learning techniques for data fusion, with applications in robotics, large-scale autonomous systems, environmental monitoring and healthcare.