Sergio Rozada

Assistant Professor at ECE, URJC. Interested in dynamical systems, reinforcement learning, and optimization.

I am an Assistant Professor in the Department of Signal Theory and Communications at Universidad Rey Juan Carlos (URJC), where I teach signal processing and machine learning and I am part of the Data Science and Signal Processing for Networks and Society group. My work sits at the interplay between data, models, and optimization: I study reinforcement learning, generative diffusion models, and graph signal processing, with the goal of understanding and controlling dynamical systems.

Background

I earned my PhD at URJC in 2025, advised by Antonio G. Marques, with a thesis on efficient methods for reinforcement learning that received the Young Researchers and Innovators Award for the best engineering dissertation at URJC. During my doctorate I was a visiting researcher in Alejandro Ribeiro’s Alelab at the University of Pennsylvania.

Before that, I studied Electronics Engineering and Industrial Automation at the University of Oviedo and Data Science at City, University of London. I spent several years in industry, working at ArcelorMittal, BBVA, Seedtag, and Meta on problems ranging from robotics and additive manufacturing to personal financial management, contextual intelligence, and large-scale data infrastructure.

Personal interests, and how I ended up here

I am a proud Asturian who spent most of his teenage years as a handball goalkeeper, trying to make it to the professional level. Spoiler: it didn’t quite work. At some point I realized that engineering hurt less and actually gave me the same kind of thrill, so I swapped the goalpost for equations.

I started out at ArcelorMittal Research, working on robotics and additive manufacturing. That is when I discovered how much I enjoyed building systems that learn from data. I moved to London for a master’s in Data Science and then joined industry to build data products: at BBVA I worked on tools for personal financial management, later at Seedtag I helped create contextual intelligence systems and dealt with machine learning at scale, and after that I worked at Meta, collaborating across teams on large scale data infrastructure and models.

In parallel I pursued a PhD in reinforcement learning and found that I really enjoy academic work, which is what eventually brought me back to the university for good.