I am an assistant professor at the mathematics department of CentraleSupélec, University Paris-Saclay in France. I am also a part of the βiomathematics group of MICS Laboratory working on machine learning methods applied in medical applications.

I received a diploma in electrical and computer engineering at the Polytechnic School of the Aristotle University of Thessaloniki. Soon after, I joined the graduate school of the University of Bern, where I conducted my PhD at the ARTORG Center for Biomedical Engineering Research under the supervisison of Prof. Stavroula Mougiakakou. There, I focused on the design of computer-assisted diagnosis support systems for interstitial lung diseases using deep learning approaches. As a part of a doctoral mobility collaboration, Ι worked with Prof. Nikos Paragios on deep learning-based medical image registration pipelines at the Central Vision Numeric (CVN) of CentraleSupélec in Paris. I continued my research on computer-assisted approaches for healthcare as a fellow of the SNSF Early Postdoc Mobility grant at Gustave Roussy in Paris. Ι worked on computer-assisted precision medicine for breast cancer in collaboration with Prof. Fabrice Andre, Prof. Eric Deutsch, Prof. Laurence Zitvogel, Prof. Nikos Paragios and Prof. Maria Vakalopoulou as part of the Centre National de Médecine de Précision (PRISM).

Email: stergios [dot] christodoulidis [at] centralesupelec [dot] fr


15 Mar 2024

Our abstract on breast cancer diagnosis from fine-needle aspiration cytology whole slide images has been accepted for a poster presentation on ESMO Breast 2024 in Berlin!

15 Jan 2024

Together with Enzo Ferantte we are looking for a PhD student within the COFUND DeMythif.AI program! You can find more details here (deadline: 24/01/2024).

13 Dec 2023

We are looking for a Postdoc to join our team and work together with Maria Vakalopoulou, Jose Dolz and Pablo Piantanida on foundation models for digital pathology and breast cancer. More details here.


Patient Derived Organoids

Quantification and biomarker discovery for patient derived organoids

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Computational Pathology

Automatic quantification and biomarker discovery of whole slide images for prediction and prognosis

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Learning Based Registration

Unsupervised registration using deep learning applied on medical and remote sensing data.

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Radiology Data Quantification

Deep Learning methods for the quantification of interstitial lung diseases.

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