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
He holds a Bachelor's degree (1987) and a Master's degree in Computer Science (1990) from the Federal University of Pernambuco, and a PhD in Electronic Engineering from the University of Kent (1994). Full Professor, since 2006, and current Director, since 2022, of the Institute of Mathematical and Computing Sciences of the University of São Paulo (ICMC-USP), in São Carlos, Full Member of the Brazilian Academy of Sciences (ABC), Research Productivity Scholarship 1A from CNPq and Deputy Director of the Center for Artificial Intelligence and Machine Learning (CIAAM) at USP. He is currently a Member of the Steering Committee for Latin America and the Caribbean of the International Network for Government Science Advice (INGSA), of the UK Expert Advisory Panel of State of Science Report for the risks associated with frontier AI, of the HealthAI Community of Practices, of the University Council of the University of São Paulo, of the Scientific Committee of the Advanced Institute for Artificial Intelligence (AI2), of the Strategy and Partnerships Board of the UKRI Center for Doctoral Training in Accountable, Responsible and Transparent AI (ART-AI), at University of Bath, UK , Associate of the Artificial Intelligence Institute - Te Ipu o te Mahara of the University of Waikato, New Zealand, the USP Budget and Heritage Commission 2024-2026, the Technological Council of the Chronos Heritage Fund Management Association (CHRONOS) and the Governance Committee Geographical Indications (GI) São Carlos and the Science for Education Network (CpE). Coordinates the IARA network, Artificial Intelligence Recreating Environments. Member of the CNPq Computer Science Advisory Committee (CA-CC), 2018-2021 (coordinator from 2019 to 2020) and 2024-2027. Coordinates the WG12.2 Working Group on Machine Learning and Data Mining of the International Federation for Information Processing (IFIP). Member of the Steering Committee of the Artificial Intelligence for Global Health (AI4GH) Project, IDRC, Canada. He was Vice-President of the Brazilian Computing Society (SBC), from 2019 to 2023. From 2013 to 2017 he was a member of the board of the International Association for Statistical Computing (IASC), of the International Institute of Statistics (ISI). He has published several articles in these areas, some of them winning awards at conferences organized by ACM, IEEE and SBC. He has written several books, including Artificial Intelligence: A Machine Learning approach, published by GrupoGen in 2011 and awarded the Jabuti 2012 award, and A General Introduction to Data Analytics, published by Wiley, in 2018. He was an Associate Professor at the University of Guelph, Canada . He was also a Visiting Professor at the University of Kent and a Visiting Researcher at the University of Kent and the Alan Turing Institute, UK. Evaluates projects for national research funding agencies: CAPES, CNPq, FAPERJ, FAPESP, FACEPE, FAPEMIG, FINEP and Fundação Serrapilheira, and international: Comisión Nacional de Investigación Científica y Tecnológica, CONICYT (Chile), Croatian Science Foundation, HRZZ (Croatia ), Czech Science Foundation, GACR (Czech Republic), Engineering and Physical Sciences Research Council, EPSRC (Kingdom United), Israel Science Foundation, ISF (Israel), Natural Sciences and Engineering Research Council of Canada, Leverhulme Trust (United Kingdom), Ministero dell'Istruzione, dell'Università e della Ricerca, MIUR (Italy) and Swiss National Science Foundation, SNSF (Switzerland). He was a member of the Governance Group of the Brazilian Artificial Intelligence Strategy (EBIA) of MCTI, of the Scientific Technical Council of the School of Applied Mathematics of Fundação Getúlio Vargas (EMAp-FGV) from 2018 to 2020 and a Member of the Advisory Council of the MCTI Network- EMBRAPII for Innovation in Artificial Intelligence. (Source: Lattes Curriculum)
| Articles published in Pesquisa FAPESP Magazine about the researcher: |
| Alta productividad |
| More itemsLess items |
| 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) |
This project defines the application of the Institutional Technical Reserve for the Maintenance and Development of Computational and Support Infrastructure for the Research Groups at ICMC-USP. The project includes investments in the Cloud-ICMC (cloud and data center infrastructure), with emphasis on the replacement of uninterruptible power supplies (UPS), stationary batteries, and power c…
This project will explore avenues for personalised healthcare planning and modelling. We will combine artificial intelligence and mathematical modelling to produce personalised diagnosis prediction, and decision support for the design of personalised treatment and diagnostic pathways. The proposed solution will ensure data privacy, fairness and model interpretability, to allow validation …
This project will create an open innovation ecosystem based on artificial intelligence for smart cities able to solve societal challenges. The ecosystem should also be an area for startups and SMEs to develop and have access to smart city solutions building on the same open source and open standards and interoperability framework recommended by EU and OASC making it attractive for both Br…
(Only some records are available in English at this moment)
This project defines the application of the Technical Reserve to maintain and strengthenICMC's Institutional and Computing infrastructure. The project includes resources formaintaining the cloud infrastructure and its datacenter, named as Cloud-ICMC, replacing theacoustic system of the Fernão Stella de Rodrigues Germano Auditorium and updating theair conditioning systems in the research e…
This project defines the Application Plan of the Portion of the Technical Reserve for Institucional Infrastructure Costs for Research - ICMC. The project includes resources for the maintenance of the cloud infrastructure and its datacenter, known as Cloud-ICMC. Cloud-ICMC has been jointly, since 2013, the general computational demands of all 33 ICMC research groups and this project will c…
This project is intended to oppiy the portion of the Technical Reserve for maintenance of the ICMC computing infrastructure. The project includes resources for the maintenance of the lnstitute's own cloud infrastructure, called Cloud-ICMC, and for improving the coverage of the wireless access network, mainly to attend advanced communication technologies. Since 2013, Cloud-ICMC has shared …
(Only some records are available in English at this moment)
Neglected tropical diseases (NTDs) are a diverse group of conditions caused by a variety of pathogens (including viruses, bacteria, parasites, fungi, and toxins) and associated with devastating health, social, and economic consequences. NTDs are abundant among impoverished communities. The lack of healthcare professionals who reach out to communities contributes significantly to delayed N…
This project explores the application of Machine Learning (ML) techniques for the diagnosis of dementia and Alzheimer's Disease (AD), aiming to establish a baseline predictive model. The project will utilize a comprehensive database with an extensive set of thematic variables, including Socioeconomic Data, Medical Diagnoses, Regularly Used Medications, Lifestyle Habits, Cognitive and Func…
Precision medicine is an approach that aims to adapt disease treatment according to the specific characteristics of each individual. To this end, it integrates information from the patient's clinical history, such as symptoms, test results, family history, and lifestyle, with the analysis of the patient's genetic profile. Due to the diversity and heterogeneity of data, different methods c…
(Only some records are available in English at this moment)
Understanding the language of life, comprised of DNA and its translation into amino acids, presents a formidable challenge for traditional methodologies due to its inherent complexity. Consequently, there has been a notable shift towards leveraging the capabilities of alignment-free techniques, particularly machine learning (ML) approaches. Among these, deep learning, specifically large l…
Autism diagnosis is made only from clinical analysis, which is often in-accessible. The use of AI could help doctors in improving the diagnosiswith biological data, such as the Functional Magnetic Resonance Imaging(fMRI). However, such complex data usually relies on the use of similarlycomplex black-box models, lacking interpretability and therefore being lessreliable. Therefore, it is im…
UN projections indicate population growth alongside increasing urban concentration, factors that stress city infrastructure. Specifically regarding sanitation, Brazil experiences 37% losses in urban water distribution, highlighting the need to modernize this system to integrate it into the context of smart cities. Additionally, it is essential to consider the need to safeguard data privac…
(Only some records are available in English at this moment)
With the advancement of modern techniques such as next-generation sequencing, the increasing amount of biological data available has posed challenges in extracting relevant molecular-level knowledge. One of the most significant challenges is predicting interactions between biological sequences such as DNA, RNA, and proteins that play crucial roles in complex processes like gene regulation…
The advancement of Computer Vision techniques, especially with Convolutional Neural Networks (CNNs), has enabled faster and more accurate medical diagnoses. However, the lack of interpretability of these models limits their clinical adoption due to a lack of transparency - particularly critical in the diagnosis of head and neck cancer. This project proposes the development and application…
A holistic perspective is vital in the search for robust Artificial Intelligence (AI) solutions. In the context of Smart Cities, AI implementations must adapt to variations in urban scenarios. Real consumption data from the city of São Paulo, obtained through a partnership with Sabesp, reveals discrepancies between the company's consumption estimation model and actual data. Exploratory an…
(Only some records are available in English at this moment)