Research
My work is at the intersection of machine learning, signal processing, and statistical modeling. A recurring question is how signal structure—and the constraints on observing, communicating, or sharing information—can inform the design and evaluation of learned representations and inference procedures.
Foundations and broader interests
These directions share mathematical questions about probabilistic modeling, representation learning, uncertainty, and inference for structured, high-dimensional data. Broader interests include reinforcement learning and the mathematical foundations of machine learning, especially where these help clarify or evaluate methods for applied problems.
Generative models for complex signals
Another area of interest is generative and foundation modeling for complex signals in communications and biomedicine, particularly behavioral and physiological measurements that may be heterogeneous or partially observed. Key questions include how models can represent signal dynamics, learn useful representations, and support inference across tasks and observation patterns.
Communication and distributed sensing
A further interest is learning and inference when information is distributed across devices, sensors, or agents. In these settings, communication, bandwidth, and decentralization constraints form part of the statistical problem, motivating representations that support a task while limiting what must be shared. Current interests include distributed inference and learning for wireless communication and sensing systems.
Papers and preprints
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2026Published
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2026Preprint
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2025Preprint
Presentations
- 25–27 January 2027
Early Artificial Intelligence and Pattern Recognition before Deep Learning on the Chinese Mainland, in Hong Kong, and in Taiwan
5th AEEAO International Congress, Universitat Autònoma de BarcelonaOral presentation
Accepted; forthcoming
Sole author and scheduled presenter.
- 7 July 2026
NARRAS
13th Spain Workshop on Signal Processing, Information Theory and Communications (SIC'26 Summer), Universidade da CoruñaPoster
Prepared and presented by Rodrigo Oliver; based on joint work with Ricardo Vázquez Álvarez, Alejandro Lancho, and Stefano Rini.
- 7 August 2025
A Foundation Model for Patient Behavior Monitoring
Machine Learning Summer School 2025, Arequipa, PeruPoster
Poster prepared by Josué Pérez-Sabater, based on our VQ-TSFM preprint; presented jointly with Rodrigo Oliver.