Roberto Cesar Jr
| 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:
Job position available: posto-doctoral scholarship on medical imaging.
Intermediate representations in Computational Science for knowledge discovery 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.