Tenured University Professor in Computer Science and Artificial Intelligence at the University of Oviedo and member of QHPC. His work focuses on computational models, formal methods, and expressive programming tools for real-world high-demand computational problems.
Alfonso Ortega De La Puente is a Tenured University Professor in the area of Computer Science and Artificial Intelligence at the University of Oviedo. Since January 2026, he has been part of the public university system in this role and is a member of the QHPC group (Quantum and High Performance Computing), where the team develops computational models and tools to address real-world problems with high computational demands.
His teaching activity includes undergraduate and postgraduate courses in imperative and object-oriented programming, artificial intelligence, machine learning (especially from symbolic and formal perspectives), algorithms, theoretical computer science, and bio-inspired computation. He has also developed dedicated research lines in computer science education, with a focus on effective classroom methodologies that increase active student engagement and improve learning outcomes in programming courses, including online and hybrid teaching settings.
His main research interest is the study of computational models such as evolutionary computation, cellular automata, and other non-classical paradigms, with the objective of providing the scientific community with practical programming tools for real-life problems. This includes work on computational approaches that can support alternatives to von Neumann-style architectures in demanding domains such as strong and ultra-strong machine learning, large-scale data processing, and big-data-compatible scenarios.
He has also worked on the computational power of formal logic as a core foundation of symbolic AI, including program-property verification (correctness and automatic generation), automated theorem proving, and formal-method-based approaches to advanced machine learning. A substantial part of his work has been devoted to programming environments for membrane-inspired computing systems, networks of evolving processors, and parallel rewriting-based computation. These contributions include textual and visual language design, user-friendly development environments, and simulator implementations for heterogeneous and massively parallel platforms.
In this context, he has contributed to expressive evolutionary automatic-programming techniques grounded in formal methods, aimed at general-purpose automatic generation of algorithms and enabling strong-learning-type solutions. He has published more than 50 scientific contributions in international journals and conferences, around ten of them in JCR-indexed venues. He has also co-supervised five successfully defended PhD theses and coordinated research activity in competitive publicly funded projects at regional and national levels.
During a period focused on teaching responsibilities, he deepened his work on programming-teaching methodologies. Motivated by the online-teaching challenges of the COVID-19 period, he also expanded his expertise in distance education through a full-digital stage as Associate Professor at UNIR (Universidad Internacional de La Rioja), later returning to the public university system.
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