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)