Welcoming Argyrios Kerezis to the AICell Lab

Please join us in welcoming Argyrios Kerezis to the AICell Lab. Argyrios joins us as a DDLS industrial PhD student, a position held jointly with ABC Labs and KTH — which means his work will sit right at the seam between academic research and real-world health data.

Who is Argyrios?

Argyrios is a biomedical engineer and physicist, with a background spanning Engineering Physics and Translational Medicine. He has spent the past years applying machine learning across healthcare and the life sciences, from clinical decision support to drug discovery, working alongside clinicians, researchers and machine learning scientists on clinical data, molecular datasets and large-scale scientific data analysis.

In his own words:

“What I enjoy most is using computational methods to better understand biological systems and help solve real scientific problems. I’m particularly interested in scientific machine learning, physics-informed AI, and hybrid modelling approaches that combine data with biological and physical knowledge.”

That combination — physics-informed thinking plus a translational instinct for what is actually useful in practice — is exactly the profile we had hoped for on this project.

What he’ll be working on

Argyrios’s research topic is Data-Driven Modeling of Human Biology, carried out together with ABC Labs within the Artemis project. Artemis brings together a broad range of health assessments and data sources to build a more complete picture of how health develops over time — a far richer view than any single test can give on its own.

Broadly, the PhD asks how much of our underlying biology can be learned from data: whether models trained at scale on diverse health data can arrive at useful internal representations of human physiology, and what those representations can tell us once we look inside them. The longer-term motivation is preventive medicine — understanding how health changes early, rather than only once something has gone wrong.

We’ll share more about the science as the work matures and results become public.

Why this fits the lab

Much of what we do at AICell Lab is about teaching models to see biology — in microscopy images, in cellular dynamics, in omics. This project asks a related question one scale up, at the level of the whole person. The methodological ingredients are familiar to us — learning from multiple data modalities, interpretability, careful FAIR data engineering — but the setting, and the preventive-health motivation, bring something new to the lab.

We’re looking forward to having Argyrios’s physics-informed perspective in the room.

Welcome aboard, Argyrios! You can read more about him on his lab profile.