Bio

Dr. Muhammad Najam Dar is a research affiliate in ophthalmology at Stanford University and AI Lead at the Ocular Imaging Research and Reading Center (OIRRC). He holds a PhD in Computer Engineering from the National University of Sciences and Technology (NUST), Pakistan, and was awarded an Erasmus Mundus Fellowship for doctoral research at St George's City, University of London, United Kingdom.

His research focuses on multimodal ophthalmic imaging, artificial intelligence, quantitative retinal biomarkers, and objective visual-function assessment, with an emphasis on translating computational methods into clinically meaningful applications. His work integrates optical coherence tomography (OCT), fundus autofluorescence (FAF), microperimetry, electrophysiological signals, and advanced image analysis to investigate spatial relationships between retinal structure and visual function.

His recent research includes the development of automated OCT- and FAF-guided microperimetry methods for spatial structure–function mapping in geographic atrophy and Stargardt disease, alongside investigations of EEG- and pupillometry-based approaches to objective visual-function assessment. These efforts aim to improve retinal disease characterization and support the development of reproducible functional biomarkers and clinically relevant research endpoints.

At OIRRC, Dr. Dar leads multidisciplinary AI development activities spanning ophthalmic image processing, multimodal image registration, retinal segmentation, human-in-the-loop AI quality control, and clinical-trial imaging workflows. His work bridges algorithm development, clinical imaging infrastructure, and the validation and integration of AI technologies into real-world research operations.

Previously, Dr. Dar served as an Assistant Professor and Lecturer, contributing to research, teaching, and mentorship in artificial intelligence, computer vision, and biomedical engineering.

His long-term research interests center on precision ophthalmology, imaging-derived phenotyping, multimodal structure–function biomarkers, and clinically validated AI systems that can advance retinal disease research, therapeutic development, and clinical-trial endpoint assessment, particularly in inherited and degenerative retinal diseases.

Academic Appointments