AI-Driven Imaging and Quantitative Phenotyping for Translational Biomedical Research

I am a faculty member in Biomedical Engineering with a research focus on the development and application of advanced imaging and AI-driven analysis methods for biomedical and translational research. My expertise spans quantitative microscopy, imaging flow cytometry, label-free imaging modalities, and deep learning approaches designed to extract interpretable, mechanistic insight rather than purely predictive outputs.

A central theme of my work is quantitative phenotyping: identifying subtle structural, morphological, and biophysical signatures associated with disease, immune response, infection, or functional state. I am particularly interested in approaches that enable scalable, reproducible analysis across large datasets and heterogeneous cohorts, including the use of explainable AI and lightweight models suitable for real-world deployment.

Within Horizon Health calls, I can contribute to projects addressing post-infection long-term conditions, vaccine development, and sex/gender differences in disease by supporting high-content cellular and tissue-level analysis, biomarker discovery, and data integration across imaging, biological, and clinical domains. I also actively engage with New Approach Methodologies (NAMs), developing AI-enabled imaging pipelines that reduce reliance on animal models while increasing biological relevance and regulatory readiness.

I am keen to collaborate with clinical, biological, and industrial partners as a methodological work-package lead or contributing partner in joint Horizon applications.