Adrian Au Laboratory

Dr. Adrian Au applies machine learning algorithms to ocular imaging modalities, including fundus photography and optical coherence tomography (OCT), to extract and quantify retinal biomarkers associated with disease phenotypes. His research emphasizes the integration of these imaging-derived features with genomic datasets to elucidate the molecular and genetic underpinnings of retinal pathology.

Through the fusion of deep learning-based image analysis and genomic frameworks, Dr. Au seeks to advance our understanding of the biological mechanisms driving disease onset, progression, and therapeutic response. His work operates at the intersection of ophthalmology, computational biology, and precision medicine, with the aim of developing clinically translatable tools for personalized diagnostics and targeted interventions in retinal disease.