- Computational statistics, applied probability & statistical signal processing
- Monte Carlo methods, including particle filtering, iterative importance sampling, Markov chain, Monte Carlo and adaptive rejection-sampling.
- Kernel density estimation.
- Stochastic optimisation.
- Uncertainty quantification & propagation.
- Nonlinear dynamical systems
- Synchronisation, control and parameter estimation in chaotic (and generally nonlinear) systems.
- Synchronisation and control of complex networks.
- Complex networks.
- Applications of statistical signal processing
- Aerospace applications: debris tracking, re-entry prediction, orbit determination.
- Distributed algorithms for wireless sensor networks.
- Navigation, positioning and tracking.
- Wireless communications: MIMO and multiuser transmission systems, interference cancellation, channel estimation, synchronisation.
- Computational biology and ecology.
Ongoing Research Projects
Machine learning and massive computation for personalised medicine and quantitative climate analysis (CLARA)
Retos Investigación 2018. Ministerio de Ciencia, Innovación y Universidades.
In this project we aim at devising classes of dynamical probabilistic models, with allied computational inference methods, which can be used to solve real-world problems in personalised medicine and quantitative climate prediction. While these two fields may look far apart, the key issues to be addressed in terms of model learning and computational inference are of the same kind. We advocate a common methodological approach to problems in both areas and expect a considerable degree of cross fertilization, with ideas and techniques that appear in one field and then can be successfully exploited in the other.
Uncertainty propagation meeting space debris needs
European Space Agency; 2019-2020
The overall goal of this work is to survey the literature on orbital uncertainty propagation (UP) methods, devise and assess new algorithms where needed, and implement a prototype for the efficient propagation of orbital uncertainties that covers all steps from initialization to the computation of a variety of specific outputs, including collision probabilities and probability distributions for re-entry times. The research is organised around 5 tasks:
- Analysis and assessment of uncertainty propagation methods
- Mapping of uncertainties into collision probabilities and re-entry times
- Design of an end-to-end processing scheme
- Prototype implementation
- Prototype qualification and tests
Advanced Bayesian computation methods for modeling and inference in complex dynamical networks (BAYTREE)
Office of Naval Research (USA); 2019-2022
Complex models, involving many subsystems that interact in non-trivial ways, appear to be ubiquitous in some of the most active fields of science and engineering. There are many difficulties yet, however, both to understand the relevant structures and schemes and to implement useful and reliable algorithms. Our first goal is to investigate a class of dynamical network-like models with layered structure. The goal is to rigorously establish the family of time series models that multilayer network structures can embed. The other class of models we intend to study includes dynamical systems which display features on very different time or space resolutions. Finally, the third aim of the project is to devise algorithms for learning, estimation and prediction that run efficiently on the models of interest. We expect that our approach, based on the joint design of the models and their associated inference algorithms, will bring improvements in accuracy and reliability for range of inference problems on complex systems.