Andreas holds a Diploma in Biology from the National and Kapodistrian University of Athens (NKUA) and an MSc Degree in Data Science and Information Technologies from NKUA's Department of Information Technology and Telecommunications. The last two years he has been a scientific collaborator at NKUA and Athena Research Center, contributing to machine learning applications in structural biology. With a strong academic background and expertise in both biology and data science, Andreas brings a unique perspective to interdisciplinary challenges. He combines his knowledge of machine learning with a deep understanding of biological systems to contribute to cutting-edge research, in order to enhance our understanding of protein structures and their function.

RESEARCH INTERESTS

Andreas' research interests include machine learning, structural biology, biophysics, computer vision, graph theory, and computational geometry. Andreas is primarily interested in applying machine learning techniques to cryo-electron microscopy and protein structure analysis, aiming to advance our understanding of biological systems by developing innovative algorithms and models.

PhD research on "Self-supervised Learning for Generalizable Particle Picking in Cryo-EM Micrographs"
Abstract: Cryo-EMMAE is a self-supervised method designed to overcome the need for manually annotated cryo-EM data. cryo-EMMAE leverages the representation space of a masked autoencoder to pick particle pixels through clustering of the MAE latent representation. Evaluation across different datasets demonstrates that cryo-EMMAE outperforms state-of-the-art supervised methods in terms of generalization.