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.
Posdoc Position Machine learning for longitudinal population studies with high-dimensional molecular measurements, with Dennis Wang (at University of Sheffield), Frank Dondelinger (at University of Lancaster) and Dave Woods (at University of Southampton). Closes: 12 August, 2020.
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.