LAD² | FCA | UNICAMP

Leonardo Tomazeli Duarte | Professor Associdado

received the B.S. and M.Sc. degrees in electrical engineering from the University of Campinas (UNICAMP), Brazil, in 2004 and 2006, respectively, and the Ph.D. degree from the Grenoble Institute of Technology (Grenoble INP, Université Grenoble Alpes), France, in 2009. Since 2011, he has been with the School of Applied Sciences (FCA) at UNICAMP, Limeira, Brazil, where he is currently an associate professor.

He is the Scientific Coordinator of the Track "Method" within the Brazilian Institute of Data Science (BI0S), one of the Brazilian Applied Research Center on Artificial Intelligence. He is also a member with the Laboratory of Data Analysis and Decision Aiding (LAD2/CPO) and with the Laboratory of Signal Processing for Communications (DSPCom lab). He is a Senior Member of the IEEE. In 2016, he was a Visiting Professor at the École de Génie Industriel (GI-Grenoble INP, France). Since 2015, he has been recipient of the National Council for Scientific and Technological Development (CNPq, Brazil) productivity research grant. In 2017, he was the recipient of UNICAMP "Zeferino Vaz" Academic Recognition Award (for research and teaching performance at UNICAMP).

Research:

His research interests center around the broad area of data science and lie primarily in the fields of signal processing, decision aiding and machine learning, and also in the interplays between these fields. He has been working on the development and analysis of methods and on applications in different areas, from geophysics to chemical sensors. His research has been funded by national and international research agencies (such as FAPESP, CNPq, CAPES, and CNRS) and also by means of partnerships with private and public institutions.

Signal Processing

Signal separation and latent variable analysis (LVA), including Independent Component Analysis (ICA), Sparse Component Analysis (SCA) and Non-negative Matrix Factorization (NMF)
Blind source separation in nonlinear models
Blind compensation of nonlinear distortions
Multi-objective optimization for signal processing
Machine learning for signal processing (Bayesian methods, neural networks, unsupervised learning)
Applications of signal processing methods (e.g. in geophysics, chemical sensors, statistical process control and sports)

Decision aiding & Machine Learning

Multiple-criteria decision analysis (MCDA)
Unsupervised (data-driven) schemes for adjusting MCDA algorithms
Models based on the Choquet integral
Interpretability and fairness in MCDA and Machine Learning
Missing data in decision methods
Applications (e.g. evaluation in sport sciences, supplier selection, problems in industry, and project selection)

Publications:

An updated list of my publications can be found at his Google Scholar page and at his Lattes CV (in Portuguese).

Teaching:

Undergraduate:

Statistics and Probability for Engineers
Biostatistics
Introduction to data science and information
Introduction to machine learning and decision aiding

Graduate:

Machine learning
Mutiple-criteria decision aiding
Foundations of data science

Contact:

leonardo.duarte(at)fca.unicamp.br

Faculdade de Ciências Aplicadas (FCA), Universidade Estadual de Campinas (UNICAMP)

Rua Pedro Zaccaria, 1300, CEP 13484-350, Limeira-SP, Brazil

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