Dimitris holds an Integrated Master's degree in Computer Science and Engineering from the Department of Computer Science and Engineering at the University of Ioannina, Greece. As part of his undergraduate studies, he conducted research for his diploma thesis, titled "Spatial Aggregation using Space Filling Curves", where he explored methods for the efficient processing of spatial queries.

His doctoral research explores the challenges of big data, with a particular concentration on vector data. In particular, his work focuses on vector similarity search (VSS), a fundamental task in modern artificial intelligence applications.

PhD research on "Efficient Approximate Nearest Neighbour Search in Vector Databases"
Abstract: We have developed a two-phase algorithm that first generates candidate sets from B+ trees like structures on each dimension, aggregating them (the candidates) via a scoring function. The second phase performs a precise, full-dimensional Euclidean distance evaluation on this pruned candidate set (only the top candidates) to determine the final approximate nearest neighbors.