SHORT BIO

Sofia Yfantidou is a PhD student at Aristotle University of Thessaloniki (AUTH) in Greece. She received her bachelor's degree in Informatics in March 2017 from the same university with a GPA of 9.47/10.0. In August 2019, she graduated from the Erasmus Mundus Joint Master Degree Programme in Big Data Management and Analytics with a GPA of 9.0/10.0, for which she was awarded a full scholarship by the European Commission. The 2-year curriculum was jointly delivered by Université Libre de Bruxelles (ULB) in Belgium, Universitat Politècnica de Catalunya (UPC) in Spain, and Technische Universität Berlin (TUB) in Germany. During her second year, she worked as a data scientist at Siemens Mobility in Berlin as part of her thesis project on "Unsupervised Prediction of Anomalous Patterns on Data Streams". Sofia is a Heidelberg Laureate Forum and a Next Generation of Women Leaders alumni, as well as a Grace Hopper Celebration of Women in Computing former scholar. Currently, she is an Early Stage Researcher for RAIS, conducting research on Prolonged User Engagement in the sports domain, trying to bridge the gap between Behavioral and Computer Science and encouraging users to adopt a more active lifestyle.

RESEARCH INTERESTS

Machine Learning, Sensor Analytics, Social Computing

RESEARCH

My research activity within the RAIS project is focused on the analysis of behavioral patterns to achieve sustained user engagement. Wearable devices and technological solutions for physical well-being suffer from high attrition rates, while having immense unexplored potential in terms of personalization at the same time. To unveil the unexploited possibilities of wearable devices, I am currently conducting an in-depth, interdisciplinary literature review, to identify current research gaps in the field of healthy habit formation and user engagement. Hence, my work lies in the intersection between Computer Science, Behavioral Sciences, and Sports Science. My goal is to utilize technology as a driver of change, rather than a secondary tool in the Internet of Sports (IoT) domain. To this end, I am planning to exploit state-of-the-art, privacy-preserving Machine Learning and analytics, to explore the habits, behaviors, motivations and barriers of people when it comes to physical well-being, and help them run more active lives in the long-term through the use of wearable technology.

PUBLICATIONS