Shuang Qian

Shuang Qian is an active academic researcher specializing in cardiac electrophysiology and computational medicine. Her work focuses on developing advanced digital twin technologies for heart disease analysis, leveraging machine learning and medical imaging (e.g., MRI) to model cardiac functions and arrhythmias. She contributes to the UN Sustainable Development Goals related to health and well-being through her research.

Her research interests include optimizing anti-tachycardia pacing therapies, non-invasive localization of ventricular tachycardia exit sites, and improving treatment strategies for ischemic heart disease and repolarization abnormalities. She collaborates extensively with global teams on porcine heart models and has pioneered computational platforms using ECG and intracardiac data to guide clinical interventions.

Shuang Qian’s publications highlight innovative approaches in cardiac modeling and virtual studies, with notable contributions to Nature Cardiovascular Research, EP Europace, and Computers in Biology and Medicine. While no specific scientific awards are listed, her work has been cited 24 times and garnered significant attention from news outlets and academic platforms like Mendeley. No information on advising students or grants is explicitly provided in the text.

She is part of collaborative networks exploring cardiac electrophysiology, involving researchers like Mark Bishop, Santiago Monaci, and collaborators from the UK Biobank project. Her studies often integrate in-silico simulations, deep learning, and clinical device data to advance heart disease treatment methodologies.