Stefanos Ougiaroglou was born in Aridea, Greece, in 1981. He received a B.Sc. in Computer Science (2004) from the Department of Informatics of the Alexander TEI of Thessaloniki, Greece, an M.Sc. in Computer Science (2006) from the Department of Computer Science of Aristotle University of Thessaloniki, Greece, and a Ph.D. in Computer Science (2014) from the Department of Applied Informatics of the University of Macedonia, Greece. The title of his Ph.D. thesis is "Algorithms and Techniques for Efficient and Effective Nearest Neighbours Classification". His doctoral studies were supported by a scholarship from the State Scholarships Foundation (I.K.Y.) of Greece. From 2016 to 2019, he conducted postdoctoral research on prototype generation algorithms for data streams and large training datasets at the Department of Applied Informatics of the University of Macedonia, Greece. Moreover, he holds a Certificate of Pedagogical and Teaching Competence (2010) from ASPETE, Greece.
Currently, Stefanos Ougiaroglou is an Associate Professor at the Department of Information and Electronic Engineering of the International Hellenic University in Thessaloniki, Greece, where he teaches courses in Programming, Algorithms and Data Structures, Databases, Web Application Development, and Data Mining. From 2022 to 2026, he served as an Assistant Professor at the same department. From 2021 to 2022, he served as an Assistant Professor in the field of Intelligent Systems at the Department of Digital Systems of the University of the Peloponnese, Greece. He has also been employed as a Laboratory Lecturer (EDIP) at the Department of Information and Electronic Engineering of the International Hellenic University (IHU), as an adjunct part-time lecturer/instructor at the Department of Telecommunication Systems and Networking of TEI of Messolonghi, Greece, and as a computer science teacher in public primary, secondary, and post-secondary education. His research interests lie primarily in Data Mining and Knowledge Discovery, Machine Learning, and Data Management and Analysis. He is particularly interested in scalable and efficient learning algorithms, instance-based learning and data reduction techniques, Automated Machine Learning (AutoML), recommender systems, and web-based data-driven systems and applications. He has co-authored numerous journal and conference papers in these areas and has served as a reviewer for several international scientific journals and conferences.