Probabilistic Machine Learning
Efficient inference methods, uncertainty quantification, Bayesian modelling and kernel methods.
Universidad Carlos III de Madrid · Signal Processing Group
Full Professor (Catedrático de Universidad) at the Department of Signal Theory and Communications, Universidad Carlos III de Madrid.
My research sits at the intersection of probabilistic machine learning, deep generative models and AI for personalised medicine and life sciences.
About
Probabilistic models for reliable machine learning
I develop machine learning methods that represent uncertainty, exploit structure and support scientific discovery. Current directions include probabilistic deep learning, generative modelling, uncertainty quantification, kernel methods, and applications in personalised medicine, omics, computational psychiatry and medical imaging.
Research interests
Methods and applications
Deep probabilistic models
Generative modelling, latent-variable models, calibrated neural networks and robust learning.
AI for life sciences
Personalised medicine, omics, computational psychiatry and medical image modelling.
MLCARE Doctoral Network
Coordinator of the MSCA Doctoral Network MLCARE, advancing machine learning for personalised medicine.
Latest news
Selected updates
Paper accepted at ICLR
Revisiting Nonstationary Kernel Design for Multi-Output Gaussian Processes. Qiaochu Xu, Zi Yang, Ying Li, Michael Minyi Zhang, Pablo M. Olmos.
Paper accepted at ICLR
A Probabilistic Hard Concept Bottleneck for Steerable Generative Models. María Martínez-García, Ricardo Vazquez Alvarez, Alejandro Lancho, Pablo M. Olmos, Isabel Valera.
Paper accepted at AISTATS — Spotlight
Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence. José Manuel de Frutos, Pablo M. Olmos, Manuel A. Vázquez, Joaquín Míguez.
Paper accepted at NeurIPS
Multi-View Oriented GPLVM: Expressiveness and Efficiency. Zi Yang, Ying Li, Zhidi Lin, Michael Minyi Zhang, Pablo M. Olmos.
MLCARE Doctoral Network
The Marie Skłodowska-Curie Doctoral Network Machine Learning Computational Advancements for peRsonalized mEdicine (MLCARE) has been granted. I am honoured to serve as project coordinator.
FBBVA Leonardo Grant
My project “THAI: Towards Humble and Discoverable AI” was awarded a grant by the FBBVA Leonardo Program.
Research group
Students, postdocs and former members
Current members
- Leire Paz — PhD student, co-supervisor, UC3M
- Óscar Jiménez — PhD student, co-supervisor, UC3M
- Josué Pérez — PhD student, co-supervisor, UC3M
- Lorena Gallego — PhD student, co-supervisor, UC3M
- Rodrigo Oliver — MSc student, co-supervisor, UC3M
- Darío Cabezas — MSc student, co-supervisor, UC3M
Former members
Contact
Get in touch
Universidad Carlos III de Madrid
Department of Signal Theory and Communications
Avenida de la Universidad 30, 28911 Leganés, Spain.
Office: 4.2.A.08 · Phone: +34 916 248 875 · Email: pamartin@ing.uc3m.es