Selected Projects
SIMAI: Synergizing Mechanistic and AI Approaches for Modeling the Impact of Microenvironment Heterogeneity on Immunotherapy Efficacy
Physically inspired generative AI for the analysis and multimodal fusion of computational histopathology data, including H&E, immunohistochemistry and spatial transcriptomics.
PhagoStat: Phagocytosis Unveiled
A scalable and interpretable deep learning framework for neurodegenerative disease analysis, focusing on phagocytosis-related mechanisms and biomedical image interpretation.
MALMO: Mathematical Approaches to Modelling Metabolic Plasticity and Heterogeneity in Melanoma
A collaborative project combining mathematical modelling, artificial intelligence and biomedical data analysis to better understand metabolic plasticity and heterogeneity in melanoma.
Visual Deep Learning-Based Explanation for Neuritic Plaques Segmentation in Alzheimer’s Disease
A weakly supervised deep learning approach for whole-slide histopathological images, focusing on explainable segmentation of neuritic plaques in Alzheimer’s disease.
STRATIFIAD: Refining Alzheimer’s Disease Patient Stratification
A computational histopathology project using explainable artificial intelligence to improve the stratification of Alzheimer’s disease patients from tissue-level features.
