Title: The machine learning revolution and its applications to robotics, mining and healthcare
I will give an overview on the latest developments in machine learning and robotics. With recent theoretical advancements in nonparametric Bayesian models and novel optimisation techniques for nonconvex problems, common tasks in multi-sensor data fusion, spatial-temporal prediction and path planning can be tackled with a unifying statistical framework. I will show how this has impacted computer vision and robotics communities and how we are creating new companies to commercialise the application of these techniques to several problems in mining exploration, environmental sciences and healthcare.
Fabio Ramos is a Senior Lecturer in machine learning and robotics at the School of Information Technologies, University of Sydney, and an ARC Discovery Early Career Fellow. 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 2007. He has over 100 peer-reviewed publications and received the Best Paper Award at IROS and ACRA. His research focuses on statistical machine learning for large-scale data fusion problems with applications to robotics, mining, environmental monitoring and healthcare.
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