CV Lattes
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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)
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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 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 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 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 …
Without being aware, we are using technologies based on Machine learning (ML) algorithms in a growing number of our daily activities. The use of ML has made many risk and tiring activities safer, more reliable and more accurate. Despite these contributions, new demands require the development of new ML algorithms, or use of these algorithms in new and innovative ways. Two important curren…
(Only some records are available in English at this moment)
This project aims to apply transformer-based architectures to analyze the movements of children withAutism Spectrum Disorder (ASD). The goal is to contribute to the existing literature by testing thesemodels and, with the aid of Explainable AI (XAI) algorithms, understanding the patterns learned bythe models and identifying potential new behavioral biomarkers.Artificial Intelligence (AI) …
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…
With the advancement of modern techniques, such as next-generation sequencing, the increasing amount of available biological data has posed challenges in extracting relevant knowledge at the molecular level. One of the most significant challenges is predicting interactions between biological sequences, such as DNA, RNA, and proteins, which play a crucial role in complex processes like gen…
(Only some records are available in English at this moment)
The use of Artificial Intelligence (AI), such as Convolutional Neural Networks (CNNs), has demonstrated efficiency and high assertiveness for medical applications. Despite this success, there are significant challenges to be overcome, related to the availability of "real-world data" and CNN architectures, which face challenges in terms of reliability and explainability, given that the use…
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…
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…
Science has been actively seeking tools to aid in the early diagnosis of diseases, including psychiatric disorders. Functional magnetic resonance imaging (fMRI) has emerged as a valuable tool for studying and diagnosing these disorders, allowing for the observation of brain activation through the BOLD signal. However, fMRI data are inherently complex, and leveraging machine learning techn…
Different microbial communities inhabit the different regions of the human body, whether mouth, gut, vagina, or others. They are collectively called the Human Microbiome (HM). These communities participate in essential processes in human health. Thus, a better understanding of the human core microbiome may help early diagnosis of disorders and contribute to potentially more efficient trea…
Forecasting is one of the main tasks of time series analysis. Selecting forecasting models is a crucial decision-making step in many real-world applications. To obtain forecasts that best explain the variables of interest in the future, it is necessary to select the model that best fits a given time series. However, the selection task can be quite computationally expensive due to many ava…
(Only some records are available in English at this moment)
| 6 / 6 | Ongoing grants |
| 27 / 19 | Completed research grants |
| 7 / 7 | Ongoing scholarships in Brazil |
| 78 / 52 | Completed scholarships in Brazil |
| 3 / 3 | Ongoing scholarships abroad |
| 15 / 15 | Completed scholarships abroad |
| 136 / 102 | All research grants and scholarships |
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