Research I am interested in the connection between Deep Learning and Probabilistic Modelling. My current research lies in creating more expressive generative models, increasing their robustness (e.g. dealing with mixed-type data or missing data) and developing better inference methods. My work has been applied to several fields, like Neuroscience or Psychiatry.
Previously I completed my PhD in Probabilistic Machine Learning at Universidad Carlos III de Madrid (UC3M), where I was supervised by Prof. Antonio Artés-Rodríguez and Dr. Pablo M. Olmos from the Signal Processing Group. Previously, I was a research intern at the Machine Learning Group in the Department of the Engineering, University of Cambridge where I worked with Prof. José Miguel Hernández-Lobato. I obtained two MSc degrees in Telecommunications Engineering and Signal Processing from UC3M, and my BSc degree in Telecommunications Engineering from Universidad de Granada (UGR).
latest news [all]
|Dec 1, 2023||I have received the Outstanding Thesis Award for my PhD thesis!|
|Oct 1, 2023||Hi from Copenhagen! I started a Postdoctoral position with Jes Frellsen’s group at the Technical University of Denmark (DTU), Denmark. Thanks again to the Danish Data Science Academy (DDSA) and to the Novo Nordisk Foundation for awarding my research project!|
|Sep 22, 2023||I successfully defended my PhD thesis! What an enriching experience. Thank you all for your support. You can find here the [manuscript] and the [slides].|
|Jul 24, 2023||Aloha ICML23! We’re presenting our paper! Come to see our poster and chat with us at Exhibit Hall 1 #432 on Thursday 27 Jul at 10:30 am (Poster Session 5)! ⏱️ We’ll explain to you how to learn distributions of functions using a VAE framework!|
|Jun 2, 2023||I gave a talk about my recent research at the Pioneer Center for Artificial Intelligence in Copenhagen, Denmark. Find here the slides.|
- Variational Mixture of HyperGenerators for Learning Distributions Over FunctionsIn Proceedings of the 40th International Conference on Machine Learning, 2023
- Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte CarloIn Advances in Neural Information Processing Systems 35, 2022
- Unsupervised learning of global factors in deep generative modelsPattern Recognition, 2022