(IEEE Member'04, IEEE Senior Member'07) received a B.Sc. degree in Physics (1996), in Electronics Engineering (1998), and a Ph.D. degree in Physics (2002) all from the Universitat de València. He is currently an associate professor (hab. Full professor) in the Department of Electronics Engineering. His is a research coordinator in the Image and Signal Processing (ISP) group
. He has been Visiting Researcher at the Remote Sensing Laboratory (Univ. Trento, Italy) in 2002, the Max Planck Institute for Biological Cybernetics (Tübingen, Germany) in 2009, and as Invited Professor at the Laboratory of Geographic Information Systems of the École Polytechnique Fédérale de Lausanne (Lausanne, Switzerland) in 2013.
He is interested in the development of machine learning algorithms for geoscience and remote sensing data analysis. He is an author of 120 journal papers, more than 150 conference papers, 20 international book chapters, and editor of the books "Kernel methods in bioengineering, signal and image processing" (IGI, 2007), "Kernel methods for remote sensing data analysis" (Wiley & Sons, 2009), and "Remote Sensing Image Processing" (MC, 2011). He's a co-editor of the forthcoming book "Digital Signal Processing with Kernel Methods" (Wiley & sons, 2017). He holds a Hirsch's index h=54 (see Google Scholar page
), entered the ISI list of Highly Cited Researchers in 2011, and Thomson Reuters ScienceWatch identified one of his papers on kernel-based analysis of hyperspectral images as a Fast Moving Front research. In 2015, he obtained the prestigious European Research Council (ERC) consolidator grant on Statistical learning for Earth observation data analysis. He is a referee and Program Committee member of many international journals and conferences.
Since 2007 he is member of the Data Fusion technical committee of the IEEE GRSS, and since 2009 of the Machine Learning for Signal Processing Technical Committee of the IEEE SPS. He is currently Associate Editor of the IEEE Transactions on Signal Processing and IEEE Geoscience and Remote Sensing Letters, and has been AE of IEEE Signal Processing Letters and invited guest editor for IEEE Journal of Selected Topics in Signal Processing (2012) and IEEE Geoscience and Remote Sensing Magazine (2015).
Main research areas
I. Machine learning for signal and image processing
II. Earth Observation, remote sensing and geoscience data processing
- Statistical learning algorithms, mainly neural networks and kernel machines
- Classification, feature selection/extraction, change/anomaly detection, and regression
- Graphical models and causal inference from empirical data
- Development of retrieval techniques for bio-geo-physical variables from imaging spectrometers
- Algorithms for remote sensing data classification and anomaly (change) detection
- Study variables affecting the terrestrial carbon cycle, land/vegetation and atmosphere modelling
Currently I'm deeply involved in a series of research projects dealing with fundamental questions in remote sensing and geosciences: model inversion, bio-geo-physical parameter retrieval, causal inference and attribution. For that I develop modern kernel methods and neural nets that encode physical prior knowledge, exploit (temporal, spatial and spectral) data structures, include signal and noise relations, multioutput and multitask inter-relations, and allow meaningful interpretation. Actually, understanding is much more challenging than prediction. This is why we study structure learning, graphical models and causal inference for climate science problems. Please visit the web pages dedicated to the projects
I'm involved in. You'll get a more detailed idea of my current and on-going research activities.
... but if you are curious about my past projects, just go here
and follow the links. My PhD thesis dealt with (deep) neural networks for unevenly sampled multitask biomedical engineering time series. This was in the early 2000. After the defence, I changed both the methodology and the application field, and focused on kernel machines for remote sensing image classification and change detection problems.