Šarūnas Girdzijauskas

Šarūnas Girdzijauskas focuses on developing decentralized AI technologies to overcome the privacy and scalability issues of centralized AI systems. His research, at the intersection of Machine Learning and Distributed Systems, aims to transition AI services to distributed frameworks, utilizing Edge AI. His team works on federated and decentralized algorithms for data mining and machine learning across distributed networks, leveraging edge computing. Additionally, Girdzijauskas contributes to Information Network Analytics and Graph Learning, crucial for analyzing linked data across social, financial, and transportation networks, as well as in fraud detection and recommendation systems. His work employs decentralized learning methods, such as Gossip Learning and Decentralized Graph Neural Networks, to process and gain insights from graph-structured data, fostering advancements in how data is analyzed and utilized in various sectors.