The main scientific area of activity is on Process Systems Engineering (PSE), even though other fields such as chemometrics and applied statistics can also be seen as targeted research areas. After an initial interest in first principle model-based fault detection and diagnostic systems, the contact with processes lacking accurate first principle models shifted the research interests towards data-driven methodologies for process analysis, modelling and improvement, as well as to other relevant issues regarding the generation and quality of data (DOE, R&R studies, measurement uncertainty). In particular, the complexity of processes and collected data motivated a concentration of efforts on the so called multiscale latent variable data-driven approaches, that present the ability of adequately describing the different phenomena going on at separate scales of time and length, through mathematical and statistical frameworks that incorporate the scale in the core of their algorithms, and where wavelet theory and megavariate statistics play key roles, when synergistically combined.
I am also been involved in developments in the fields of: Multivariate Image Analysis (MIA), process monitoring (batch, continuous and profiles), analysis of gene expression data, systems biology, process improvement & six-sigma (industry, healthcare, tourism), modeling of the dispersion of pollutants in soils, wine ageing, beer shelf stability, pulp & paper (formation, piping streaks, cockle, dimensional stability and curl), monitoring of control loops performance, analysis of data from the semiconductors industry.