Stelios Perrakis

I was born and raised in Chania, Crete, where I became interested early in science, math, and problem-solving. Growing up in a place that values independence and resilience shaped how I work today, with discipline, curiosity, and responsibility for what I do.

I later moved abroad to study and do research, which meant adjusting to new academic systems, cultures, and ways of working. Living and studying outside my home country pushed me to become more independent and comfortable with uncertainty. It strengthened my habit of learning on my own and my preference for environments where progress comes from real effort rather than titles or formality. Over time, my studies focused on computer science and applied machine learning, especially problems that connect theory with real use cases.

I completed my MSc in Computer Engineering at the University of Crete (GPA: 9.14/10), where I specialized in computer vision, neural networks, and pattern recognition. My master’s thesis focused on generative models for super-resolution in medical imaging, which I developed during my time as a Research Fellow at the Foundation for Research & Technology Hellas (FORTH).

I am currently a PhD candidate at CentraleSupélec, University of Paris-Saclay, where I work on deep learning methods for digital pathology. My research focuses on developing techniques for large medical image datasets under weak supervision, in settings where labels are limited, data are noisy, and decisions matter. I am particularly interested in generative models, representation learning, and their applications to real-world clinical problems.

Keywords: Digital Pathology, Generative Models, Deep Learning, Computer Vision