Tereso del Rio Almajano

Tereso del Rio Almajano is a Researcher at the Research Institute for Symbolic Computation (RISC), Johannes Kepler University, Linz, Austria. His work focuses on optimizing Cylindrical Algebraic Decomposition (CAD) algorithms through machine learning and heuristic development to address doubly exponential computational complexity in symbolic computation.

He earned his PhD from Coventry University's Centre for Computer Science and Mathematical Modelling under supervisors Dr. Matthew England, Dr. AmirHosein Sadeghimanesh, Prof. Vasile Palade, and Dr. Kamal Bentahar. His dissertation, "Heuristics and Machine Learning to Improve Symbolic Computation Algorithms: Speeding Up Quantifier Elimination", pioneered methodologies for accelerating CAD while ensuring output validity.

His research spans Symbolic Computation, Machine Learning, and Computer Algebra, with specific expertise in Quantifier Elimination and variable ordering optimization. Key contributions include dataset augmentation techniques that reduced CAD construction time by 38%, the state-of-the-art gmods heuristic derived from complexity analysis, and the first application of Explainable AI in symbolic computation to extract human-interpretable heuristics from machine learning models.

As part of RISC, he addresses computational challenges in algebraic geometry and quantifier elimination, demonstrating how machine learning can surpass human-designed heuristics in symbolic computation efficiency while maintaining mathematical certainty.