Sanjiv Narayan
Sanjiv M. Narayan is a British-born American physician, biomedical engineer, and academic researcher. He is a Professor of Medicine at Stanford University. Narayan's work is focused on treating patients with heart rhythm disorders, particularly those with atrial fibrillation. His research applies bioengineering and computational methods to develop improved diagnostic tools and therapy. Narayan is a fellow of the American Heart Association; the American College of Cardiology, the Heart Rhythm Society, and the Royal College of Physicians of London. He is a Section Editor for "Journal of the American College of Cardiology". Education. Narayan completed his medical  training in 1987 and his Masters in Computer Science in 1990 from the University of Birmingham, UK with a thesis on "Restricted Connectivity in Neural Networks". He received membership of the Royal College of Physicians (MRCP) that year. During post-doctoral research at the University of California, Los Angeles he developed systems and software to image and map intrinsic optical signals in rodent somatosensory cortex under Arthur W. Toga. Narayan completed residency in internal medicine at Mount Auburn Hospital/Harvard, fellowship in Cardiology and Cardiac Electrophysiology under Michael Cain and Bruce Lindsay at Barnes Hospital/Washington University. Career. Narayan was  faculty at University of California, San Diego from 2001-2014, and at the University of California, Los Angeles from 2012-2014. In 2014, Narayan joined Stanford University. Research. Narayan’s research is focused at the intersection of clinical cardiac electrophysiology and bioengineering. From the late 1990s to early 2000s he studied tissue mechanisms for complex heart rhythm disorders by studying rate-dynamics of monophasic action potentials and conduction patterns in patients with atrial fibrillation, ventricular fibrillation and controls. Narayan pioneered efforts for panoramic mapping of atrial fibrillation using global multipolar mapping catheters and artificial intelligence (AI) algorithms to separate physiological  signals from noise. This work revealed localized drivers for AF, as targets for therapy. His later work has been focused on using AI to bridge basic arrhythmia mechanisms to patient care. He directs the Computational Arrhythmia Research Laboratory (CARL).