Profesores e Investigadores

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Curriculum Vitae

Sergio Rozada Doval
Profesor/a Ayudante Doctor/a

Centro

E. Ingeniería de Fuenlabrada

Departamento

Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación

Área

Teoría de la Señal y Comunicaciones

Grupo de investigación

Grupo de investigación de alto rendimiento en ciencia de datos y procesamiento de señal y su aplicación a las redes y la sociedad de la URJC / Data Science and Signal Processing for Networks and Society group of URJC

Contacto

sergio.rozada@urjc.es
Presentación

  • I am an Assistant Professor in the Department of Signal Theory and Communications at Universidad Rey Juan Carlos (URJC) in Madrid, where I teach signal processing, optimization, and machine learning.

    I earned my PhD at URJC in 2025, having the honor to be advised by Prof. Antonio G. Marques. My thesis, Low-Rank Methods in Reinforcement Learning, received the Young Researchers and Innovators Award for the best engineering dissertation at URJC. During my doctorate I was a visiting researcher in Prof. Alejandro Ribeiro¿s Alelab at the University of Pennsylvania. I received my MSc in Data Science from City, University of London in 2018, and my BSc in Electronics Engineering and Industrial Automation from the University of Oviedo in 2015. Before returning to academia, I spent some years in industry at ArcelorMittal, BBVA, Seedtag, and Meta, developing data and machine-learning products.

    My research interests encompass reinforcement learning, generative diffusion models, graph signal processing, and tensor and low-rank methods. I exploit the low-rank structure hidden in value functions and policies to make reinforcement learning tractable in large state-action spaces, and I recast dynamic programming and generative diffusion through the lens of graph signal processing. These perspectives turn classical signal processing tools into parsimonious models with provable guarantees for sequential decision making and for data defined over irregular domains.

    My ultimate goal is to use insights from signal processing and optimization to characterize the mathematical foundations, develop novel architectures, and devise efficient learning procedures that let us understand and control complex dynamical systems.

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Agencia Nº documentos Nº citas Índice H Q1 D1 IFNB IFNESI
Logo de la agencia 'Web of Science' Web of Science 12 28 3 2 - - -
Logo de la agencia 'Scopus' Scopus 12 33 4 3 2 0,07 -
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