From Scientific Research to Clinical Innovation
My translational work focuses on the development of responsible and trustworthy artificial intelligence for computational pathology, biomedical imaging and multimodal medicine. At the interface between academic research, clinical needs and industrial innovation, my objective is to design AI systems that are not only technically performant, but also interpretable, reproducible and clinically useful.
Computational Pathology
Development of AI methods for digital pathology, whole-slide image analysis, virtual staining, tissue phenotyping and clinically relevant decision support.
Multimodal Biomedical AI
Integration of histology, spatial omics, molecular data and clinical information to build richer representations of disease and support precision medicine.
Trustworthy & Responsible AI
Emphasis on explainability, robustness, reproducibility, regulatory awareness and human-in-the-loop systems for real-world biomedical deployment.
VivaTech 2023
At Viva Technology 2023, our team presented research-driven innovation in digital pathology and AI-assisted biomedical image analysis. The demonstration highlighted how advanced deep learning, efficient computation and clinically oriented design can help reduce repetitive tasks, improve analytical consistency and support healthcare professionals in their decision-making workflows.
This experience illustrated a broader vision: translating cutting-edge academic research into robust and usable technologies for medicine, while maintaining scientific rigor, transparency and responsibility.
