CV Lattes
ResearcherID
ORCID
Google Scholar Citations
Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
holds a Bachelor's degree (2001), Master's degree (2003), PhD (2007), and Postdoctoral degree (2009) in Computer Science and Computational Mathematics from the University of São Paulo. He participated in the CAPES doctoral program with an overseas internship (2005-2006) at Carnegie Mellon University in Pittsburgh, PA, USA. Currently, he is an Associate Professor (MS5.3) at the Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos campus. Between 2019 and 2020, he completed a postdoctoral period at the University of Grenoble-Alpes, France, specializing in Artificial Intelligence applied to medicine, having published about ten works ranging from predictive medicine to pathology classification. He is a member of the Brazilian Computer Society (SBC) and a regular reviewer and author in his field, contributing to publications in major journals and conferences. His professional activities focus on Analytical Science, Image Processing, and Databases, with a primary emphasis on topics such as visual data analysis (computer graphics applied to databases), machine learning, artificial intelligence, and analytical science, applying these techniques in the fields of medicine, agriculture, and e-learning. (Source: Lattes Curriculum)
| News published in Agência FAPESP Newsletter about the researcher |
| More itemsLess items |
| TITULO |
| Articles published in other media outlets ( ): |
| More itemsLess items |
| VEICULO: TITULO (DATA) |
| VEICULO: TITULO (DATA) |
The proposal focuses on the challenges of managing and integrating multimodal health data for knowledge discovery, aiming at extracting meaningful information from large volumes of complex and diverse health data by leveraging Big Data and Artificial Intelligence (AI) for the Health Sciences. Almost every human activity now generate and require storing and processing vast quantities of di…
The collaborative research project on Artificial Intelligence for Improved Infectious Diseases Outcomes in Kidney Transplant Recipients (AIIDKIT) represents an initiative that leverages extensive kidney transplant datasets from Brazil and Switzerland to propel predictive modeling capabilities in the realm of transplantation medicine. By capitalizing on the wealth of data originating from …
The computational technology of the XXI, characterized by the Web 2.0, century has provided powerful resources to generate digital content, massive access to information, and connectivity between systems, people and electronic devices. This technology produces data in unprecedented scale and it has received different denominations: big data, web scale, massive data or planetary scale. A r…
(Only some records are available in English at this moment)
This project focuses on Artificial Intelligence for enhanced outcomes in infectious diseases among kidney transplant recipients; it taps into rich datasets from Brazilian and Swiss kidney transplants to advance predictive modeling in transplantation medicine. Utilizing data from varied patient demographics and healthcare frameworks, the project aims to advance the prediction of infection …
Complex networks contribute toward computational research and analysis through their ability to design systems modeled by vertices and edges, and by the attributes of their elements. They provide means to describe urban structures through their street mesh, expressing predicates that refer to the flow and transportation in an urban zone. The analysis of such structures is potentialized wh…
Since the birth of web 2.0, users no longer just consume, but are now active creators of content that is going to be consumed by other users. This new dynamic took data generation to a whole new scale, called planetary scale or web scale. Often, this data represents relationships between its elements, such as in social networks, recommendation systems, online boards, email networks, scien…
The processing of large-scale graphs - so-called planetary-scale, or Web-scale, graphs - has attracted the interest of several applications such as e-commerce, computer networks, social networks, biology (protein interaction), among many others. In order to process these graphs, which are composed of millions of vertices and billions of edges, it has become usual to use distributed proces…
(Only some records are available in English at this moment)
Deep learning (DL) has proven to succeed in a wide range of domains, ranging from acoustics and images to natural language processing. However, applying deep learning to graph-like data is a non-trivial task because of the unique properties of graphs. Recently, a significant amount of research efforts have been devoted to this area, notably improving graph analyzing techniques. Employing …
Deep Learning (DL) describes a class of algorithms capable of combining raw inputs into successive layers of intermediate features to achieve computational intelligence. These algorithms have shown impressive results across several domains. In Medicine, for example, which is a data-rich discipline, the data are complex and often not yet understood. Deep Learning techniques may be particul…
Although there is a growing literature that tackles the unsupervised outlier detection problem, the unsupervised evaluation of outlier detection results has been notably overlooked. In contrast to the unsupervised cluster analysis, where indexes for internal evaluation and validation of clustering solutions have been conceived and shown to be very useful, in the outlier detection domain o…
(Only some records are available in English at this moment)
| 2 / 2 | Ongoing grants |
| 6 / 5 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 9 / 5 | Completed scholarships in Brazil |
| 4 / 4 | Completed scholarships abroad |
| 22 / 17 | All research grants and scholarships |
| Associated processes | |