My research is related to
statistical learning, mainly kernel machines and neural networks, for Earth Observation
and remote sensing data analysis.
I'm interested in understanding
human vision from information theoretic principles. This statistical view has
implications in experimental and computational neuroscience. (See the ex-cathedra statement)
My interests are related to
machine learning and signal and image processing. The application domains are remote
sensing data analysis and hyperspectral images with special focus on cloud screening.
At present I'm focused on kernel
methods, support vector machines, semi-supervised and active learning. The main
application field is on remote sensing data. I have recently worked with one-class
classifiers applied to hyperspectral images.
My current research involves
signal processing to develop new interactive man-machine interfaces and haptics, and
real-time multispectral sensors and actuators. I'm also with Analog Devices Inc.
Senior research scientists
Maria Piles - Ramon y Cajal Researcher
interests include microwave remote sensing, estimation of soil moisture and vegetation
biogeophysical parameters and development of development of multisensor techniques for
enhanced retrievals with focus on agriculture, forestry, wildfire prediction, extreme
detection, and climate studies.
Veronica Nieves - Distinguished Researcher
research at the interface between ocean sciences and climate informatics focuses on the
development of the next generation of advanced algorithms to better understand our
changing oceans and evaluate potential future climate risks. Research activities can be
followed on MALOC
I'm working on image fusion,
developing downscaling and pansharpening methods. The goal is to tackle multitemporal
image fusion and change detection problems with improved resolution.
The topic of my thesis was
Distance Metric Learning. Currently, I am working on kernel methods, dependence
estimation and Machine Learning in general. I am interested on the applications of
Machine Learning techniques to solve the Remote Sensing challenges.
I am conducting fMRI recordings of the
visual brain using synthetic and natural images. I am director of the
Clinic of the Universitat de Valencia
, which has a range of experimental
tools for vision research.
José J. Esteve-Taboada
I am developing
computational models of the visual function based on functional Magnetic Resonance
Imaging, psychophysics, and image statistics.
I'm working in image statistics and
vision science. I have developed several methods for density estimation, measure
independence, manifold learning and visual quality assessment.
Ana B. Ruescas
I am a data scientist and project
manager. As a remote sensing specialist I have experience in several application areas
like ocean colour and thermal algorithm development and validation.
José Enrique Adsuara
Currently, I am working on
learning parameters of differential equations, HPC for linear solvers, and causality. I
am interested in machine learning, especially statistical learning and deep neural
My current research focuses
on cloud screening in multispectral imagery using machine learning techniques.
Previously I was also involved in the algorithm improvement for the retrieval of soil
moisture and vegetation parameters from microwave observations.
Álvaro Moreno Martínez
My research has been mainly
focused on remote sensing applications in vegetation. I have been actively involved in
the development of physical and statistical models and the implementation of operational
methodologies for the study of vegetation cover through satellite imagery at different
Miguel Ángel Fernández
During the Ph.D. period, my
research was related to spatio-temporal visual attention modeling and understanding,
applying both Bayesian networks and deep learning. I work on the design of explainable
visual attention models and machine attention mechanisms to be deployed in anomaly and
extreme event detection.
My research focus on probabilistic
graphical models: Bayesian networks/DAG, Gaussian graphical models, staged event trees.
Recently I am interested in structural recovery from spatio-temporal data, causal
discovery and applications for the Earth sciences.
Eva Sevillano Marco
ISP Coordination & Project Manager. Ecologist by training. My research interests lie in Remote Sensing & GIS applications: multisensor support to forestry inventories, agriculture, land cover and change detection, geospatial data quality, Earth Observation products & services, etc. Collaborations with outstanding research groups and institutions, an asset. Onboard for new challenges!
My background is in the field
of applied machine learning. I am specially interested in applications to natural
sciences like remote sensing and weather and energy forecasting. Currently I am working
in transfer learning with convolutional neural networks applied to cloud and flood
Anna Maria Mateo
I am working on developing machine
learning algorithms for Earth Observation global monitoring. My current research is the
incorporation of physical knowledge and multivariate output regression methods.
Jose Antonio Padrón
My PhD thesis work is about
developing a new family of anomaly change detection algorithms for remote sensing image
processing and geoscience time series analysis.
J. Emmanuel Johnson
My background is in enhancing
manifold learning and alignment algorithms for improved dimensionality reduction and
data fusion of remote sensing hyperspectral images. My current research involves feature
learning and dependence estimation using kernel methods and semi-supervised learning for
applications in Earth observation data.
My research interests include kernel methods, graphical models and causality, with the focus on Earth science applications.
The main goal of my research is to
develop automatic algorithms for the detection of clouds from remote sensing images. I
mainly focus on the inclusion of prior knowledge and (spatial, temporal, angular)
constraints in machine learning classifiers.
My research work is about machine
learning and its application on remote sensing specifically in feature extraction and
climate dynamics analysis. I am especially interested in soil moisture data analysis and
I'm currently conducting my PhD within the MALOC project on the development of statistical, physical and hybrid methods to study the regional effects of climate change in the oceans. I have a background in Earth observation data and machine learning methods.
Javier Martínez Amaya
I joined the team to help
with the practical implementation of climate-related assessment tools and data sets
currently in development within the MALOC project. My background is in remote sensing
techniques, analysis and applications.
I'm working on computational neuroscience
via combined fMRI and image processing techniques to better understand the mechanism of
the human vision system, which efficiently processes and extracts the information from
the natural world.
I am pursuing my PhD thesis in the
iMIRACLI project on hybrid and interpretable machine learning for cloud-aerosol
I've worked with machine mearning
methods for crop yield estimation using climate and multi-sensor remote sensing data. My
current research is now focused on the use of deep learning to upscale high resolution
My research interests focus on kernel methods and the incorporation of physical knowledge in statistical methods to understand and improve Earth system modelling. My current research involves detection and attribution of climate change processes.
My research interest is tied to study feature representations that are sparse, interpretable and causal. At ISP I am working on the H2020 DeepCube project developing modules for interpretability and explainability in ML models.
Jose Maria Tárraga
My research interest is to study the impact of climate change on human mobility through machine-learning methods. At ISP I am working on the H2020 DeepCube Climate Induced Migrations use case.
Is this you?
We are always looking for
smart people! Send us your resume and ideas of collaboration!
Daniel Heestermans Svendsen
I am working on machine
learning methods for remote sensing and earth observation data. My current focus is on
kernel methods, and the incorporation of physical knowledge in statistical methods.
I am working on convolutional
versions of linear + nonlinear models of visual neuroscience and using those in visual
prosthesis and image quality metrics.
I'm working on causal discovery from
observational data, and in particular to study climate-induced human migrations.
At IPL I've
been working on computational visual neuroscience, modelling the processes that take
place in the brain from the retinal images until we get information out of them.
My primary research interests lie in
the area of Monte Carlo methods for Bayesian inference. My specialty is focused on the
random number generation problem and computational methods for stochastic quadrature,
such as rejection sampling, MCMC algorithms and importance sampling techniques.
My current research interests are
related to statistical methods for the detection and the attribution of climate change
and especially for the attribution of extreme weather events. Attribution methods
usually rely on the analysis of observations and climate model experiments.
I'm with the UVERS group,
doing my PhD thesis on biophysical parameter retrieval for crop monitoring, in
particular in the FP7 ERMES project, and collaborating with ISP people on GP retrieval
In my postdoc at the ISP group, I
addressed a number of machine learning problems related to hyperspectral image
processing including graph adaptation, active learning, and advanced kernel methods.
In my PhD I applied advanced
contrast perception models as regularization functionals to solve inverse problems such
as image restoration and motion estimation and studied their connection to image
My PhD work (best-thesis award in
Physics and Maths 2003) was focused on perceptual and statistical image representations
for image coding and texture classification.
My work at the ISP group
included the development of multiinformation and divergence measures using
In my MSc work I applied accurate
contrast perception models to improve Support Vector Regression in subjective domains
for image coding.
In my MSc thesis I worked with a
Kernel generalization of the SSIM image quality index well suited to be applied to
In my PhD years (best-MSc thesis
award in Computer Science 2013) I analyzed the complexity of spatio-spectral signals for
illumination invariant Bayesian reflectance estimation and hyperspectral image coding.
During my stay at ISP I used with
Kernel Ridge Regression for image denoising assuming smoothness in the spatial domain.
In my MSc thesis I applied
nonlinear models of chromatic contrast perception in wavelet domains to improve
During my MSc work I contributed to
develop nonlinear local-to-global Independent Component Analysis.
My work at the ISP group included
the analysis of multi-temporal remote sensing image changes, and the definition of
advanced one-class classifiers.
My work at the ISP group included
the development of semi-supervised one-class classifiers for remote sensing data
My work at the ISP group included
the development of target detection algorithms for remote sensing data analysis.
At ISP I built virtual worlds of
controlled spatial arrangement to study the effects of occlusion, perspective and view
point in 2D shape statistics.
in my MSc Thesis I developed in cortical
image representations that are simultaneously robust to neural noise and energy
In my stay at IPL I addressed the
problem of decoding the visual signals from simulated and real neural responses.
My PhD work included kernel-based
nonlinear generalization of classical (linear) feature extraction techniques to improve
classification results in remote sensing.
During my a year visit to IPL, I focused
on processing optical images using kernel methods and variational methods, large scale
anomaly change detection methods and digital terrain model (DTM) extraction. It was a
joy to live in Valencia and life-time experience to work with productive and cheerful
researchers in the IPL.
My research interests include machine
learning and signal processing, especially deep neural networks and transfer learning.
Currently I am working on applying physical constraints to ML models for better
generalization, consistency and extrapolation capabilities.