Roberto is a professor of the
University of Sao Paulo (USP) since 1998 (BSc in
Computer Science - UNESP - 1992; MSc in Electrical
Engineering -UNICAMP - 1993; Ph.D. in Physics -
USP/Brazil,IPT-UCL/Belgium - 1997; Visiting researcher
ENST/Telecom ParisTech/Paris,France) . He is currently a
Full-Professor in the Department of Computer Science -
IME - USP. He is currently special advisor for Physical
Sciences and Engineering at the Sao Paulo Research
Foundation - FAPESP. He served as the Director of the
eScience Research Center at USP and as the head of the
Computer Science Department. He was member of the Image
and Vision Computing and the Signal, Image and Video
Processing editorial boards, chair and invited speaker
of conferences and workshops (Sibgrapi 2003, CIARP 2010,
Sibgrapi 2011; SHAPES 2.0 - 2012, eSon - IEEE eScience
2013, IEEE eScience 2014). He has experience in computer
science, with emphasis on computer vision, machine
learning and artificial intelligence.
Opportunities
Our lab has ongoing projects with interesting open problems for
students (undergraduates, MSc, PhD) and researchers (Post-doc,
Sabbatical) willing to join us. There are interesting
opportunities for fellowships in these levels (including Post-doc,
Young Researcher and Sabbatical). Note that the fellowship
conditions are competitive in international levels. Please do not
hesitate in contacting me in case you become interested in working
with us. Students and collaborators from all countries are quite
welcome!!!! (e.g. see the standard deviation of my co-authors :-)
Come South, young scientist! Please check the ongoing projects below with open opportunities:
This project focuses on a unified strategy for knowledge and
emerging dynamics discovery in Computational Science using
intermediate representations. The intended applications are in
areas characterized by large volumes of data in which knowledge
discovery implies the transition from raw data bases for
intermediate representations (usually feature vectors and graphs),
thus allowing for the subsequent use of different analytical
methods. In this context, integration and transformation methods
to be used in the generation of intermediate data should also
ensure the quality and reliability of data generated for the
intermediate representation. The results of the analysis phase may
influence both experiments and the integration methods for
generating new data by feedback mechanisms. This project has two
general goals: 1) to develop methodologies to solve Computational
Science problems based on a common approach of intermediate
mathematical-computational representations; 2) to apply the
developed methodologies to different scientific problems, thus
creating specific solutions to each problem. This methodological
strategy will be used to address specific problems in areas which
our group has been working in recent years: intermediate
representations in computer vision and urban informatics; study of
biological networks dynamics to characterize the mechanisms of the
health-disease transition; development of computational tools for
processing of MRI images high field and their integration with
biological data; development of new techniques for
characterization and visualization of intermediate representations
in complex dynamic networks, with applications in Systems Biology.
(AU)
Spatio-temporal analysis of pediatric magnetic resonance images
FAPESP ANR joint project with ParisTech, Universite Dauphine
et Faculte de Medicine Paris Sud
The advances in medical imaging require to develop quantitative or
semi-quantitative methods to improve accuracy in the image
analysis results. Advances in medical im- age analysis
provide such tools, but there is still an important gap regarding
pediatric brain imaging, even though there is an increasing
medical demand. This project aims at contributing to fill this
gap, focusing on brain magnetic resonance imaging (MRI) of in-
fants, newborns and premature babies, which raise specific issues
due to the particular grey/white
matter contrast related to the physiological myelination process,
the very fast but not continuously observed evolution of the brain
structures and possible pathologies, and the high intra-and
inter-subjects variability. One of these issues is that the data
at hand are noisy, ambiguous, scarce in nature and sparse in time.
In turn, expert medi- cal knowledge is available, but
is prone to change and evolution. From this point of view the
project tackles one of the very cutting edge questions in data
analysis, that is how to extract and understand meaningful
patterns where the data are scarce but expert knowl- edge,
continuously enriched, is available. We propose to develop
structural representations of knowledge and image information in
the form of graphs and hypergraphs, which will be exploited to
guide spatio-temporal image understanding (segmentation,
recognition, quan- tification, comparison over time, description
of image content and evolution). The aim is to aid diagnosis,
pathology analysis and patients’ follow-up. Applications will
include the analysis of hyperintensities on the white matter, the
volumetry of corpus callosum and its evolution, and neuro-oncology
with the study of the influence of tumors on surrounding
structures over time. The project involves specialists in medical
image analysis, structural knowledge representation and pediatric
neuro-imaging.
Affiliation
University of Sao Paulo - USP
Institute of Mathematics and Statistics - IME
Computer Science Department
Address
Rua do Matao 1010
Cidade Universitaria
05508-090 - Sao Paulo, SP - Brasil
Phone: +55 11 30916135