I am a Postdoctoral Researcher at Inria Saclay, where I am part of the MIND (Models and Inference for Neuroimaging Data) team. My work lies at the intersection of deep learning, neuroimaging, and neuroscience. I aim to develop models that learn robust, generalizable representations of the brain from large-scale datasets, supporting both clinical neuroscience and cognitive modeling.

Before joining Inria, I was a postdoctoral researcher at University of Turin, Italy, in the EIDOS group at the Computer Science Department, working on deep learning for medical imaging, with interests on contrastive learning, neuroimaging, multimodality, debiasing and robustness. I received a double Ph.D. from Télécom Paris (Institut Polytechnique de Paris) and University of Turin in 2023, supervised by Pietro Gori (Télécom Paris) and Marco Grangetto (University of Turin).

News:

• Nov. 2025: I am now a Postdoctoral Researcher at Inria Saclay, MIND team.
• Jan. 2025: I am currently a Visiting Researcher at Inria MIND with Prof. Demian Wassermann, to study the connection between functional connectivity and cognition using contrastive learning.
• Dec. 2024: I am happy to announce that I am now a member of ELLIS Society 🎉!
• Mar. 2024: I am member of the newborn Asl To3 Radiomics Lab in Rivoli, Italy.

Ongoing Research

My research focuses on three main areas at the intersection of deep learning and neuroimaging. If you are interested in collaborations do not hesitate to contact me!

Contrastive Representation Learning

Learning robust, generalizable representations through contrastive objectives for medical imaging and neuroimaging. Self-supervised learning, unbiased contrastive frameworks, and multi-site harmonization.

Collateral Learning and Debiasing

Developing robust AI systems by identifying and mitigating spurious correlations in biased training data. Human-interpretable explanations of discovered biases for trustworthy clinical AI.

Foundation and Normative Modeling

Pre-training on large-scale neuroimaging datasets to learn generalizable brain representations. Effective transfer to rare diseases and small clinical populations.

Positions

• Nov. 2025 - now Postdoctoral Researcher at Inria Saclay, MIND team
• Jan. 2025 - Feb. 2025 Visiting Researcher at Inria MIND
• Dec. 2023 - Oct. 2025 Postdoctoral Researcher in deep learning for medical imaging (University of Turin)

Education

• 2020-2023 Ph.D. in Computer Science at Télécom Paris, France (cotutelle with University of Turin, Italy), with the thesis Collateral-Free Learning of Deep Representations: From Natural Images to Biomedical Applications.
• 2018-2020 Master’s degree in Artificial Intelligence at University of Turin, Italy

Selected Publications

• Anatomical foundation models for brain MRIs, C. A. Barbano, M. Brunello, B. Dufumier, M. Grangetto. Pattern Recognition Letters, 2025. [Paper]
• Contrastive learning for regression in multi‑site brain age prediction (Best poster award), C. A. Barbano, B. Dufumier, E. Duchesnay, M. Grangetto, and P. Gori. ISBI, 2023. [Paper]
• Unbiased Supervised Contrastive Learning, C. A. Barbano, B. Dufumier, E. Tartaglione, M. Grangetto, and P. Gori. ICLR, 2023. [Paper]
• Integrating Prior Knowledge in Contrastive Learning with Kernel, B. Dufumier, C. A. Barbano, R. [Paper] Louiset, E. Duchesnay, and P. Gori. ICML, 2023
• End: Entangling and disentangling deep representations for bias correction, E. Tartaglione, C. A. Barbano, and M. Grangetto. CVPR, 2021. [Paper]