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.
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.
New preprint, Gaussian Process Regression for Binned Data, with Michael Smith and Neil Lawrence.
New preprint, Bayesian inversion of a diffusion evolution equation with application to Biology, with Jean-Charles Croix and Nicolas Durrande.
Paper at Expert Systems with Applications, Tensor decomposition processes for interpolation of diffusion magnetic resonance imaging, with Hernán Vargas, Andrés Álvarez and Álvaro Orozco.
Paper at IEEE Transactions on Automatic Control, Gaussian process latent force models for learning and stochastic control of physical systems, with Simo Särkkä and Neil Lawrence.
Paper at NeurIPS 2018 (as a spotlight), Heterogeneous Multi-output Gaussian Process Prediction, with Pablo Moreno-Muñoz and Antonio Artés-Rodríguez.
Paper at MLSP 2018, Nonlinear Probabilistic Latent Variable Models for Groupwise Correspondence Analysis in Brain Structures, with Hernán F. García and Álvaro Orozco.
Paper at UAI 2018, Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian Processes, with Cristian D. Guarnizo.
Paper at AISTATS 2018, Differentially Private Regression with Gaussian Processes, with Michael Smith, Max Zwiessele and Neil Lawrence.