Fabio Ramos School of Information Technologies University of Sydney Location: Carslaw 173 Time: 2pm Friday, October 7, 2011 Title: Making sense of the World with Machine Learning for Data Fusion Abstract: Physically grounded problems are typically characterised by the need to jointly infer multiple quantities from various sensor modalities, at different space and time resolutions. As an example, consider the problem of estimating a real-time spatial-temporal model of pollution dispersion in a river using mobile platforms. Given the technology available, the vehicle can sense biomass, temperature, PH and many other chemical/physical quantities. Understanding the relationships between these quantities can significantly improve the accuracy of the method while reducing the uncertainty about the phenomenon. In this talk I will show a set of techniques for nonparametric Bayesian modelling that address the challenges in spatial-temporal modelling with heterogenous sensors. In particular, I will show: 1) how to define exact and sparse models that are scalable to large datasets; 2) how to integrate data collected at different support and resolutions; and 3) how to automatically learn relationships between different quantities in real-time, from mobile platforms. I will show applications of these methods to a number of problems in robotics, mining and environment monitoring. Short bio: Fabio Tozeto Ramos 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. From 2007 to 2010 he was an Australian Research Council (ARC) research fellow at the Australian Centre for Field Robotics (ACFR). In 2011, he commenced as a Senior Lecturer in machine learning at the School of Information Technologies, University of Sydney. He has over 70 peer-reviewed publications and received the Best Paper Award at the International Conference on Intelligent Robots and Systems (IROS) and at the Australian Conference on Robotics and Automation (ACRA). He is an associate editor for ICRA and IROS, and a program committee member for RSS, AAAI and IJCAI. His research focuses on statistical learning techniques for large-scale regression and classification problems, stochastic spatial modelling, and multi-sensor data fusion with applications in robotics and mining. He leads the Learning and Reasoning group at ACFR.