LUCIANO
SANCHEZ RAMOS
University Professor
Department: Informática
Institute/Center: Instituto Universitario de Tecnología Industrial de Asturias
Universidad: University of Oviedo
Area: Computer Science and Artificial Intelligence
Research group: MYM Metrología y Modelos
Email: luciano@uniovi.es
Phone: +34 985 182130
Address: Edificio Departamental Oeste, módulo 1, Despacho 1.1.28, Campus de Gijón, s/n. C.P. 33204, Gijón (Asturias, España)
Doctor by the Universidad de Oviedo with the thesis Control difuso de procesos industriales mediante una arquitectura paralela y distribuida, tipo red neuronal 1994. Supervised by Dr. José Antonio Corrales González, Dr. Hilario López García.
Luciano Sánchez is a Full Professor of Computer Science at the University of Oviedo, Spain, and holds a PhD in Industrial Engineering from the same university. He leads the Metrology and Models research group and is a co-founder of the spin-off company Idalia Intelligent Data Analysis. He has served as Director of the institutional Chair in Data Analytics and Artificial Intelligence in partnership with TotalEnergies and has held visiting research positions at the University of California, Berkeley, and at GE Global Research in the United States. His research focuses on the theoretical and applied study of algorithms for mathematical modelling and intelligent data analysis, with particular emphasis on incomplete and uncertain data, condition monitoring and industrial reliability, generative models and soft computing, and explainable and sustainable AI. He combines fundamental research with technology transfer and has led research projects and R&D contracts with companies in the energy, transport, industrial and healthcare sectors, including collaborations with Rolls-Royce, TotalEnergies, RENFE and Medtronic. He has received, among other distinctions, an IEEE Outstanding Paper Award and an Engineering Innovation Prize from Rolls-Royce. He publishes regularly in indexed and open-access journals and serves on editorial boards, programme committees and national evaluation panels. Research interests Applied Artificial Intelligence · Intelligent data analysis · Incomplete and uncertain data · Condition monitoring and industrial reliability · Generative models and soft computing · Explainable and sustainable AI