Fabio Ramos, Professor
School of Computer Science
University of Sydney

Modern robotic platforms provide immense amounts of data that need to be quickly integrated into probabilistic models representing the environment autonomous systems operate in. In this talk I will show statistical machine learning methods for online spatial and spatial-temporal learning that are able to integrate information from heterogeneous sources, scaling gracefully to very large datasets. With these representations, I will demonstrate how Bayesian optimisation and the principle of modelling uncertainty can be used to mitigate risks in decision making for motion planning and policy search in dynamic environments.

Fabio Ramos is a Professor in robotics and machine learning at the School of Computer Science, University of Sydney, where he leads the Learning and Reasoning Group. He is also co-director of the Centre for Translational Data Science (CTDS) at the University of Sydney. He received the BSc and MSc degrees in Mechatronics Engineering at University of Sao Paulo, Brazil, in 2001 and 2003, respectively, and the PhD degree at the University of Sydney, Australia, in 2008. His research focuses on statistical machine learning techniques for large-scale data fusion with applications in robotics, mining, environmental monitoring and healthcare. He has over 130 peer-review publications and received Best Paper Awards at IROS, ECML, ACRA and Best Paper Finalist at RSS.