Dr. Amit Kumar Garg is a Postdoctoral Associate at Yale School of Medicine, where he conducts research as part of the Cardiovascular Data Science (CarDS) Lab. His work lies at the intersection of cardiovascular medicine, clinical informatics, and artificial intelligence, with a focus on translating data-driven innovations into routine clinical care.

Dr. Garg completed his medical training (MBBS) at the All India Institute of Medical Sciences (AIIMS), New Delhi, India's leading medical school. During the COVID-19 pandemic, he worked on developing machine learning models for COVID-19 detection, an experience that sparked his enduring interest in applying artificial intelligence to solve real-world healthcare challenges. During his time at AIIMS, he also served as Finance Secretary of PULSE, the institute's annual cultural and literary festival, where he managed financial planning and operations for one of the largest student-led events in the country. Following graduation, he worked as a Medical Officer at AIIMS, New Delhi, before transitioning into research at the intersection of artificial intelligence and clinical medicine. Prior to joining Yale, he led the validation, and deployment of AI solutions for medical imaging and healthcare, focusing on translating machine learning research into real-world clinical practice through large-scale validation studies and clinical implementation.

His current research focuses on comparative effectiveness research and the deployment and evaluation of artificial intelligence models within electronic health record (EHR) systems to improve cardiovascular care. His work leverages large-scale real-world clinical data to compare therapeutic strategies, assess AI performance in routine clinical practice, and develop robust frameworks for integrating machine learning models into healthcare workflows.

Dr. Garg's broader research interests span cardiovascular imaging, clinical informatics, medical natural language processing, multimodal data integration, and the deployment of trustworthy AI in clinical workflows. He is particularly interested in bridging the gap between AI model development and real-world clinical implementation to improve patient outcomes and support evidence-based clinical decision-making.

Outside of research, Amit enjoys exploring new technologies, reading, running, traveling, playing badminton, and mentoring students interested in artificial intelligence and medicine.