Statistics Seminar: Fabio Ramos -- Making sense of the World with Machine Learning for Data Fusion

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.