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, Recyclable Gaussian Processes, with Pablo Moreno-Muñoz and Antonio Artés-Rodríguez.
Accepted paper at NeurIPS 2020, Multi-task Causal Learning with Gaussian Processes, with Virginia Aglietti, Theodoros Damoulas and Javier González.
New paper at Applied Sciences, A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis, with Cristian Torres-Valencia, Álvaro A Orozco, David Cárdenas-Peña and Andrés Álvarez-Meza.
Accepted paper at AISTATS 2020, Black-box Inference for Non-linear Latent Force Models, with Wil Ward, Tom Ryder and Dennis Prangle.
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.