CV Lattes
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Universidade Estadual Paulista (UNESP). Campus de Bauru. Faculdade de Ciências (FC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Currently, he is a Full Professor in the Computer Science Department at São Paulo State University "Júlio de Mesquita Filho", Unesp-Bauru. He holds a Bachelor's degree in Information Systems from São Paulo State University "Júlio de Mesquita Filho" (2002), a Master's degree in Computer Science from the Federal University of São Carlos (2005), a PhD in Computer Science from the State University of Campinas (2008), and postdoctoral studies in Computer Science at the State University of Campinas (2009), Harvard University (2015), and the Massachusetts Institute of Technology (MIT) (2025). He is a senior editor of the journal IEEE Signal Processing Letters, and a member of the editorial board of the journals Computer Methods in Biomechanics and Biomedical Engineering, Journal of Next Generation Information Technology, and International Journal of Bio-Inspired Computation. He is co-editor of a book in the area of #8203;#8203;metaheuristic optimization and collaborates with several international institutions. He has been a visiting professor at the Ostbayerische Technische Hochschule (Germany) and Coordinator of the Postgraduate Program in Computer Science at Unesp. He is a Fellow member of the International Association for Pattern Recognition (IAPR), the Asia-Pacific Artificial Intelligence Association (AAIA), and the Alexander von Humboldt Foundation (AvH), as well as the Brazilian representative to the International Association for Pattern Recognition (IAPR) and a senior IEEE member. In 2025, he was awarded the Ibn Sina Global Scholarship from the King Fahd University of Petroleum and Minerals (KFUPM), Kingdom of Saudi Arabia, and the International Excellence Fellowship from KIT - Karlsruhe Institute of Technology, Germany. He was a full member of the Special Committee on Computer Graphics and Image Processing (CEGRAPI) from 2015 to 2018 and chair of the IEEE Task Force on Business Intelligence and Knowledge Management. He has experience in the field of Computer Science, with an emphasis on Computer Systems, mainly working on the following topics: image processing, machine learning, and pattern recognition. He also participates in projects in the area of #8203;#8203;Petroleum Engineering at the Center for Geosciences Applied to Petroleum (UNESPETRO) - Unesp/Rio Claro, and Recogna - Unesp/Bauru. He is currently a member of CA-CC/CNPq and Coordinator of the eScience and Data Science Program at FAPESP. (Source: Lattes Curriculum)
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The proposal presents CePEX - São Paulo Center for Data Integration for Monitoring Extreme Events, which aims to develop integrated research on monitoring, forecasting, and decision support for disaster events in São Paulo. The proposal seeks to use analytical models consolidated in the literature, advanced Artificial Intelligence techniques, and multiple data sources, which include a div…
This project investigates alternative representations of qubits (quantum bits) using complex analysis, with a focus on the parametrization z = e^{i¿}·tan¿, which directly relates the quantum state |¿¿ = ¿|0¿ + ¿|1¿ and the visual representation of the state |¿¿ as a function of z through the projection of a colored domain onto the Bloch Sphere. The project aims to formalize and mathematic…
The Data Science Center for Smart Industry acts as an advanced data analytics hub for industry, aiming to improve operational efficiency, product quality, innovation and sustainability through the application of data science and analytics techniques of data in industrial processes. Contact with industries will be mediated by SENAI and operationalized through workshops and thematic meeting…
(Only some records are available in English at this moment)
Recent neurological discoveries on the cortex, the hippocampus, and different areas of the brain shed light on pyramidal cells, a neuronal architecture composed of five layers, i.e., Soma, Basal dendrites, Apical dendrites, Axon, and Collateral axon, whose interaction is responsible for the forward and backward flow of information, as well as the integration among context and memory, amon…
Barrett's esophagus (BE) comprises a very severe and growing disease in the last decades, and since BE is often not identified properly at the early stages, it may evolve to a more aggressive version, and even to cancer. However, the early diagnosis of dysplastic tissue in BE-diagnosed patients may provide very high rates of remission after the treatment. In the past few years, deep learn…
Undiagnosed diabetic retinopathy (DR) leads to vision impairment and blindness, and its early detection can significantly reduce severe vision impairment by 50%. The presence of exudates in fundus images is one of the first signs of such a disease, but their manual detection is examiner-dependent and time-consuming. In this proposal, we aim at coping with the lack of data concerning compu…
(Only some records are available in English at this moment)
Advancements in medical imaging technologies have revolutionized clinical diagnostics, providing modalities such as X-rays, computed tomography (CT), and magnetic resonance imaging (MRI), each offering unique perspectives on human anatomy. However, the diversity of sources, acquisition protocols, and patient populations results in misaligned data and discrepancies in imaging patterns, pos…
Oral cancer represents a serious public health problem in Brazil, with a high annual incidence and a still-high mortality rate, especially due to late diagnosis. Squamous cell carcinoma (SCC) is the most prevalent type, primarily affecting men over 60 years of age and affecting areas such as the tongue, floor of the mouth, and lips. Despite the simplicity of the clinical examination, fail…
In recent years, artificial intelligence (AI) has advanced significantly, driven by the development of hardware with greater processing capacity and the consequent evolution of Natural Language Processing (NLP) models. In the medical context, although Large Language Models (LLMs) demonstrate considerable potential, their implementation faces challenges such as judgment biases, limitations…
(Only some records are available in English at this moment)
This project aims to develop an application that promotes communication accessibility for people who are deaf or hard of hearing. The proposal is to use artificial intelligence (AI) technology to convert texts typed by users into a visual translation into Brazilian Sign Language (Libras). The application will include an interactive digital avatar, capable of clearly reproducing the signs …
Amyotrophic Lateral Sclerosis (ALS) is an incurable and progressive neurodegenerative disease impacting motor neurons, leading to bulbar dysfunction in over 80% of cases. Survival rates after diagnosis vary from 20 to 48 months, with only 5 to 10% of patients living beyond 10 years, underscoring the critical need for early detection. However, the absence of reliable clinicopathological ma…
Chest X-ray images play a crucial role in modern medicine, being an accessible and easy-to-use tool for radiologists to identify diseases and assess the proper positioning of medical devices. Ensuring the correct positioning of these devices is vital for the patient's health, especially in high-risk cases where X-ray examinations are frequent. With the advance of deep learning techniques …
(Only some records are available in English at this moment)
This project aims to explore flaws in anonymity techniques via deep learning algorithms and display the current issues with open-access datasets. Radiography is an essential technology in the medical field, and its effects on the landscape of diagnosis of different problems cannot be understated. The fast advancement of machine learning techniques, coupled with this intrinsic technology, …
Brazil is the world's largest sugarcane producer and sugar supplier, an important economic activity. Weed control must be adequate, for herbicides are likely to damage the culture. The problem worsens on perennial plants, e.g., sugarcane, since not cleaning the soil after a harvest is a widespread practice. Plants and weeds often get mixed, making their automatic differentiation a severe …
Over the years, supervised image classification has made some noble advances. The classification models, in turn, developed a stronger need for larger datasets. However, in many situations, images are scarce. In this context, few-shot learning stands out as it allows models to learn from a few labeled samples. In particular, a promising model known as Prototypical Networks was introduced.…
Esophageal adenocarcinoma is an illness that is usually hard to detect at the early stages in the presence of Barrett's esohagus. The development of automatic evaluation systems of such illness may be very useful, thus assisting the experts in the neoplastic region detection. With the strong growth of machine learning techniques aiming to improve the effectivess of medical diagnosis, the …
(Only some records are available in English at this moment)
| 7 / 7 | Ongoing grants |
| 17 / 17 | Completed research grants |
| 11 / 11 | Ongoing scholarships in Brazil |
| 45 / 33 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 6 / 6 | Completed scholarships abroad |
| 87 / 75 | All research grants and scholarships |
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