CV Lattes
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Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. Instituto de Ciência e Tecnologia (ICT) (Institutional affiliation from the last research proposal) Birthplace: Brazil
graduation at Análise de Sistemas from Universidade Estadual do Centro-Oeste (2007), graduation at Matemática from Universidade Estadual do Centro-Oeste (2008), master's at Computer Science from Universidade de São Paulo (2011) and doctorate at Computer Science from Universidade de São Paulo (2016). He is currently full professor at Hospital das Clínicas da Faculdade de Medicina da USP. Has experience in Computer Science, focusing on Information Systems, acting on the following subjects: base de regras fuzzy, conjuntos fuzzy, trypoxylon lactitarse saussure, grafos com parâmetros fuzzy and problema de caminho mínimo. (Source: Lattes Curriculum)
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The algorithmic bias remains one of the key challenges for the wider applicability of Machine Learning (ML) in healthcare. Statistical modeling of natural phenomena has gained traction due to increased representation capacity and data availability. In medicine, particularly, the use of ML models has increased significantly in recent years, especially to support large-scale screening and…
The digital transformation will play a fundamental role in overcoming the contemporary challenges faced by the Industry, including the efficient use of natural resources, increasing the efficiency of processes, adding value to its products through digital services, and reorganizing the work with the introduction of new tools, among others. As one of the highlights in terms of digital tran…
Graph-based semi-supervised learning (SSL) is a promising paradigm for modeling the manifold in multidimensional data space, and is effective in propagating a small number of initial labels to a large amount of unlabelled data. This approach has been used in a variety of applications, such as image segmentation and annotation, human speech recognition, text classification, etc. Recently, …
From a data science perspective, Industry 4.0 represents a paradigm shift in the Fourth Industrial Revolution, enabling the extraction of valuable knowledge through the adoption of intelligent monitoring and data fusion strategies. Additionally, it allows for the application of advanced machine learning and optimization methods. One of the main objectives of data science in this context i…
In traditional supervised machine learning, the goal is to develop a model that becomes successful and efficient in a task based on example data. Typically, specific models are built that excel only in solving a single specific problem. This traditional development approach incurs a high time cost to create, train, and fine-tune models from scratch for each new problem, even if it is a pr…
In the financial market, the speed and impact of information such as news, social media posts and press releases play a crucial role in the decision-making of investors, companies and governments. Events such as interest rate changes, earnings reports or political and economic developments generate rapid reactions that result in price fluctuations of financial assets. The growing influenc…
(Only some records are available in English at this moment)
Predictive maintenance is the periodic monitoring of equipment or machines, through data collected in field measurements such as temperature, vibration, thermography, etc. This maintenance predicts the lifetime of machine components, preventing failures by monitoring equipment in operation. With this, we have several benefits, such as predicting the need for maintenance of equipment, redu…
As Machine Learning (ML) gains popularity in many domains like healthcare and industrial equipment monitoring, the need for transparent decision-making models becomes paramount. In industrial settings, timely maintenance of equipment is crucial for uninterrupted operations and cost savings. Leveraging machine learning, predictive and prescriptive maintenance endeavors to predict and preve…
The use of Artificial Intelligence (AI) through Machine Learning (ML) in everyday life is a reality in several applications, from recommendation systems, financial systems and even in medicine. Randomized trials estimate the mean treatment effects for a population, but participants in clinical trials are often not representative of the patient population receiving treatment in terms of …
(Only some records are available in English at this moment)
| 2 / 2 | Ongoing grants |
| 3 / 3 | Completed research grants |
| 5 / 5 | Ongoing scholarships in Brazil |
| 15 / 15 | Completed scholarships in Brazil |
| 25 / 25 | All research grants and scholarships |
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