About Me

Welcome to my personal website! I’m Iason (/iason/) Ofeidis, a second-year Ph.D. student in Electrical and Computer Engineering at Yale University. Before joining the Ph.D. program, I worked as a Research Engineer at the Yale Institute for Network Science (YINS). My advisor is Prof. Leandros Tassiulas.

I’m broadly interested in the intersection of Artificial Intelligence and networks, with a focus on how modern learning systems can become more efficient, scalable, and accessible in distributed environments. My research centers on Split Federated Learning for multimodal systems, where I design methods that integrate large language models (LLMs) and vision models under diverse communication and computation constraints. A core theme of my work is bringing large AI models closer to small, resource-constrained devices—developing architectures, training pipelines, and system-level frameworks that help democratize big AI for the edge. I’m particularly interested in collaborative AI paradigms, including federated learning, split learning, and hybrid forms that enable devices to learn collectively while preserving efficiency and privacy.

Beyond this, I have worked on network pruning and model partitioning for edge efficiency, as well as comparative studies of data-loading systems for Deep Learning. My earlier research also includes applying data science to examine the impact of U.S. monetary policy on Decentralized Finance (DeFi).

I completed my undergraduate studies at the Aristotle University of Thessaloniki (AUTh) in Greece, earning an M.Eng. in Electrical and Computer Engineering with specializations in Telecommunications. My diploma thesis focused on how Machine Learning can predict the emotional state of smartphone users by utilizing only their keystroke characteristics, and my advisor was Prof. Leontios Hadjileontiadis. Prior to joining Yale, I worked as a Research Assistant at the Institute of Applied Biosciences (INAB) at the Centre for Research and Technology (CERTH) in Greece. There, I led the software engineering operations, restructuring data processing, and integrating it into libraries for distribution among bioinformaticians.