I am interested in machine learning in general, its interplay with mathematics and statistics and its applications. In particular, my research interests include probabilistic models, kernel methods, and stochastic processes. I work on the development of new probabilistic models and their application in different engineering and scientific areas that include Neuroscience, Neural Engineering, Systems biology, and Humanoid Robotics.
New preprint, A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model with Juan-José Giraldo.
New preprint, Continual Multi-task Gaussian Processes, with Pablo Moreno-Muñoz and Antonio Artés-Rodríguez.
Accepted paper at NeurIPS 2019, Multi-task Learning for Aggregated Data using Gaussian Processes, with Fariba Yousefi and Michael Smith.
New preprint, Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes, with Wil Ward, Tom Ryder and Dennis Prangle.
Accepted paper at IEEE/ACM Transactions on Computational Biology and Bioinformatics, Physically-inspired Gaussian processes for transcriptional regulation in Drosophila melanogaster, with Andrés F. López-Lopera and Nicolas Durrande.
Accepted paper at ICASSP 2019, Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain, with Pablo Alvarado and Dan Stowell.
Accepted paper at AISTATS 2019, Non-linear process convolutions for multi-output Gaussian processes, with Wil Ward and Cristian Guarnizo.