Summary
Sergio Rozada is an Assistant Professor in Signal Theory at Universidad Rey Juan Carlos, combining eight years of industry and academic experience in machine learning, reinforcement learning, and diffusion generative models. His research probes why modeling choices work, emphasizing the interplay of optimization, data, and low-rank model structure—work grounded in a PhD on low-rank methods and tensor algebra for dynamical systems. He brings production ML perspective from roles at Meta, BBVA, and Seedtag, where he built large-scale pipelines and a +40-model system serving 1K+ QPS. As an educator and mentor, he connects signal processing and data science theory to practical engineering through teaching and programs like Zrive’s Applied Data Science. Based in Madrid, he balances rigorous theory with product-minded execution, favoring research questions that lead to deployable systems. A bilingual engineer with a Distinction MSc from City, University of London and early R&D experience in additive manufacturing, he retains a hands-on background from C++ robotics to Python ML stacks.
8 years of coding experience
9 years of employment as a software developer
Bachelor's degree, Industrial Electronics and Automation Engineering (Bilingual degree), Bachelor's degree, Industrial Electronics and Automation Engineering (Bilingual degree) at Universidad de Oviedo
Master's degree, Data science, Distinction, Master's degree, Data science, Distinction at City, University of London
Doctor of Philosophy - PhD, Electrical, Electronics and Communications Engineering, Cum laude, Doctor of Philosophy - PhD, Electrical, Electronics and Communications Engineering, Cum laude at Universidad Rey Juan Carlos
Spanish, English