Fernando Almaraz Menéndez is Professor at the Faculty of Economics and Business at the University of Salamanca, the institution where he has developed his professional career and where. In addition to carrying out his teaching and research work, he has held various management positions, including Director of Innovation and Digital Production (2010-2017) and Secretary General (2018-2021). He is specialized in the digital transformation processes of Higher Education Institutions. He completed a degree in Mathematics at the University of Salamanca, and his training then drifted towards new technologies and business management, completing his studies at the School of Telecommunications Engineering of the Polytechnic University of Madrid (Master's Degree) and at the University of Cordoba (PhD). He holds an Executive MBA from FENA Business School, as well as other postgraduate courses from MIT, IESE and ESADE.
Alexander Maz Machado, Degree in Mathematics and Physics (1992). Specialist in Mathematics Education (1997). PhD from the University of Granada in the Department of Didactics of Mathematics (2005). He is a tenured lecturer in the Department of Mathematics at the University of Cordoba. His research interests are History of mathematics and mathematics education, science evaluation and bibliometrics, attitudes and beliefs towards mathematics and curriculum analysis in higher education. He has coordinated the SEIEM Research Group on History of Mathematics and Mathematics Education for more than ten years. He directs the Research Group Mathematics, Education and Society: SEJ-589 of the Andalusian Research Plan. He is editor of the journals Epsilón and MES. He has been principal investigator of national and regional R+D+i projects. He has published more than a hundred articles and monographs. Since 2010, he is a member of the board of directors of the Andalusian Society of Mathematical Education "Thales". He has supervised eleven doctoral theses and some thirty master's theses. He participates in research evaluation committees in Spanish and foreign agencies.
Carmen López Esteban, BSc and MSc in Mathematics from the University of Salamanca (1985); PhD in the Doctoral Program Educational Reforms in the History of Education, with the research work "The initial training of teachers in Arithmetic and Algebra through textbooks" - University of Salamanca (2011). Professor and researcher in Didactics of Mathematics, her areas of research development focus on historical research and also on research in teacher training processes. She is currently directing a research project on "Teacher Education on Sustainable Development Goals (SDGs)". She is the author of books, book chapters and articles in indexed academic journals. She is an Associate Professor at the School of Education of the University of Salamanca where she is Head of the MSc in Secondary Teaching Education with Qualified Teacher Status (QTS), since 2012.
Cristina Almaraz López (Salamanca, 1995). Research & Development Engineer at the New Frontier Center of ArcelorMittal Global R&D of Avilés, Asturias, her work focuses on the application of Deep Learning techniques to real-world scenarios in the industry of steel-manufacturing. She studied a Bachelor's Degree in Computer Engineering in Information Technology and a Master's Degree in Computer Engineering at the Information Technologies at the Polytechnic School of Engineering of Gijón (University of Oviedo, Spain). In both she obtained several extraordinary prizes for the best academic records. During the Master's program she spent a semester at the University of Texas (UTEP) where she first became interested in Artificial Intelligence and Deep Learning. She has also studied different minor degrees on many varied topics, including a program on Digital Transformation at La Salle University and a course on business administration taught by the IÉSEG School of Management in Paris. She is the author of the book “Deep Learning applied to Computer Vision. Main concepts, historical development and state of the art”. Her research interest include Deep Learning applied to Computer Vision, Transfer Learning and Semi-Supervised Learning, and Ethics of Artificial Intelligence.