Shishir Rao

Shishir Rao is a senior AI research scientist within the Deep Medicine research group led by Professor Kazem Rahimi specialising in deep learning applications for medical risk prediction and outcome analysis. He completed his DPhil at the University of Oxford under Professor Kazem Rahimi's supervision, following a Master of Science in Computer Science from Oxford and a dual bachelor's degree in Mathematics and Computer Science from New York University.

Currently, Rao co-leads multiple AI-driven healthcare initiatives with Rahimi, focusing on perinatal risk assessment, understanding musculoskeletal conditions, and understanding heart failure. His research combines cutting-edge AI approaches with clinical applications, particularly emphasising:

Transformer-based deep learning architectures for multimodal electronic health records (EHR) analysis, pioneering new approaches to medical data integration and interpretation. He has pioneered the BEHRT model, the first Transformer model for multimodal EHR. His work extends to developing novel frameworks for causal inference in complex healthcare settings, with a particular focus on explainable AI methods for clinical decision support.

Rao's research portfolio includes significant contributions to cardiovascular risk prediction and the study of multimorbidity patterns in long-term conditions. His interdisciplinary approach bridges the gap between advanced computational methods and practical clinical applications, aiming to improve patient care through innovative AI solutions.