Download Machine learning for data fusion and the Big Data question Abstract

Title: Machine learning for data fusion and the Big Data question
Abstract:
Recent years have seen an enormous proliferation and availability of data
typically collected from sensors embedded in computer devices. Most people
have mobile phones with an integrated camera, GPS and accelerometers.
Mobile robots can possess radars, 3D laser scanners, and hyperspectral
cameras. Larger-scale examples include the mining industry where several
sources of information (core drilling, cone logging, spectrometry) are available
to predict the ore concentration and assess the quality of the product.
However, immense quantities of data are not necessarily useful unless we
develop methods to interpret and represent multi-modal information
efficiently. In this talk I will present methods 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. I will show a
set of techniques for nonparametric Bayesian modelling that address the
challenges in spatial-temporal modelling with heterogenous sensors. In
particular: 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 exploration and
environmental monitoring.
Short bio:
Fabio Ramos is a Senior Lecturer 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. From 2007 to 2010 he was
an ARC research fellow at the Australian Centre for Field Robotics (ACFR).
He has over 80 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, IROS, RSS and a program
committee member AAAI, IJCAI and IPSN. His research focuses on
statistical machine learning for large-scale data fusion problems with
applications to robotics, mining, environmental monitoring and healthcare.