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Universidade Estadual de Campinas (UNICAMP). Faculdade de Ciências Aplicadas (FCA) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Leonardo Tomazeli Duarte earned his degree in Electrical Engineering from the University of Campinas (Unicamp) in 2004. He holds a Masters degree in Electrical Engineering from Unicamp (2006), a Ph.D. from the Institut Polytechnique de Grenoble (Grenoble INP - Université Grenoble Alpes, France) (2009) in the Signal, Image, Parole, Télécom program, and a habilitation (Livre-Docência) from Unicamp (2020). He completed a postdoctoral fellowship at Université Toulouse III Paul Sabatier (France) in 2023. He is an Associate Professor (MS 5.2) at the School of Applied Sciences (FCA) of Unicamp. His research focuses on artificial intelligence, machine learning, signal processing, and multicriteria decision support. He is a Principal Investigator at the Applied Research Center for Artificial Intelligence BI0S (CPA-FAPESP - Brazilian Institute of Data Science). He served as Coordinator of the Graduate Program in Production and Manufacturing Engineering (PPGEPM) at FCA (2015-2017) and as Graduate Studies Coordinator at FCA (2017-2021). He also acted as Advisor to the Office of the Vice Provost for Research (PRP) at Unicamp (2021-2023). Since 2025, he has been serving as Advisor to the Executive Board of Information and Communication Technology (DETIC) at Unicamp, where he coordinates the Reference Center for Artificial Intelligence Technologies. He is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and a member of the technical committees Signal Processing Theory and Methods (SPTM) and Machine Learning for Signal Processing of the IEEE Signal Processing Society (IEEE-SPS). He has been a visiting professor at the École de Génie Industriel (Grenoble INP, France) in 2016, Université Paris Cité in 2024, and Toulouse INP in 2025. He received the Zeferino Vaz Academic Recognition Award from Unicamp in 2017. In 2022, he was elected Affiliate Member of the Brazilian Academy of Sciences (ABC) for the 2023-2027 term, in the field of Engineering Sciences. (Source: Lattes Curriculum)
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The Brazilian Institute of Data Science (BI0S) is a research center with the main purpose to develop state-of-the-art solutions in data science and artificial intelligence (AI), solving relevant problems and connecting academia, companies, startups, society and the public sector in an integrated innovation ecosystem. It will contribute to scientific, technological and social development, …
Blind source separation (BSS) refers to the task of recovering a set of source signals in an unsupervised fashion, that is, by only considering a set of mixtures of these sources. This field, which has been extensively studied in signal processing and fields related to data analysis, encompasses a wide range of applications, from the separation of biomedical signals to feature extraction …
A central problem in multi-criteria decision analysis is to develop quantitative methods that sort a set of alternatives by taking into account different decision criteria. These methods have been intensively applied in problems from different domains. However, despite the impressive results obtained in multi-criteria decision analysis and its central role in the area of operations resear…
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Blind source separation (BSS) has been extensively studied in the fields of signal processing and machine learning. The goal of BSS is to recover a set of signals (sources) solely from the observation of mixtures of these sources, which constitutes an unsupervised learning task. Aiming to contribute to the search for new optimization strategies in the context of BSS, we shall investigate …
A theme that has been gaining prominence in the scientific literature is the development of artificial intelligence (AI) algorithms-especially machine learning-adapted for execution on quantum computers, with the goal of benefiting from potential computational performance gains provided by quantum computing. One of the central challenges in this area is that the quantum and classical para…
Decisions in various domains, including personal, social, and governmental, are typically made by considering multiple relevant aspects. These aspects may include decision criteria, conflicting objectives, or even multiple modes of information, such as multimedia. Decision-making involving multiple aspects often presents challenges as it requires significant effort in data analysis and r…
A central issue in the study of machine learning method regards the search for interpretability in models obtained after a training phase. Indeed, in additional to providing to the decision maker elements about how the model is using the information available to accomplish a given task, interpretability is a crucial requirement for more transparent algorithms. Motivated by the current eff…
Machine learning techniques have been used in the construction of automatic systems in order to deal with several practical problems. Examples include the applications in credit systems, which evaluate if an individual will lead to a possible default with respect to the received credit, or in judicial systems, which predict whether the defendant under trial may re-offend. In general, the …
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Currently, the research community has been putting an effort into the development of mechanisms that improve interpretability and fairness in machine learning problems. Recent works addressed interpretability by means of model-agnostic method called SHAP. This method is based on the Shapley value, a classical concept in cooperative game theory that indicates the marginal contribution of a…
Control charts are commonly employed to determine whether the variability observed in a process stems from natural causes or assignable causes, such as machine failure. When an assignable cause is detect, it is quite helpful to classify the nature of such a cause, since this information is useful to tackle the original problem. This task can be accomplished by automatic pattern recognitio…
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