Surbhi Sharma is a postdoctoral researcher, working under the guidance of Dr. Patrick M Boyle in the CardSS lab, where she is advancing the field of cardiovascular health through innovative applications of AI/ML. With a robust interdisciplinary background in machine learning, modeling, and optimization, Surbhi’s career has been driven by a commitment to addressing critical medical challenges. During her doctoral research Surbhi developed computational and machine learning models to analyze virus-immune interactions, significantly contributing to drug dosing and vaccine optimization.
Currently in her postdoctoral training, Surbhi is leading a project to develop a deep learning-based decision support system for early detection of cardiomyopathy in pediatric cancer survivors. This research aims to identify subtle dynamic changes in cardiac function associated with earlier stages of cardiomyopathy. This study shows the potential of advanced deep learning techniques to predict future cardiomyopathy, well before clinical diagnosis, enabling timely intervention and improving patient outcomes for high-risk pediatric cancer survivors. Additionally, Surbhi is spearheading a project focused on developing machine learning techniques to identify risk factors leading to sudden cardiac arrest in a general population. Surbhi is dedicated to translating advanced AI/ML techniques into practical tools for healthcare, reflecting her passion for leveraging technology to improve clinical outcomes and patient quality of life.