Han Feng

Dr. Feng is a Research Assistant Professor at Tulane University School of Medicine and an independent cardiovascular data science investigator with expertise in biostatistics, machine learning, causal inference, and real-world evidence analysis. His research sits at the interface of clinical electrophysiology and quantitative methodology, with a central focus on transforming complex cardiovascular data into clinically actionable evidence to understand atrial fibrillation mechanisms, model disease progression, and personalize treatment strategies. Trained in biostatistics and machine learning, Dr. Feng integrates clinical insight with advanced analytic methods across large-scale electronic health records, Electrocardiograms, wearable sensors, cardiac imaging, ablation procedures, clinical trials, and genetic data. At Tulane, he has played leading scientific, quantitative, and operational roles in major cardiovascular research studies, including HEARTBEAT and CASTLE-HFpEF. He also leads a multidisciplinary cardiovascular data science team of postdoctoral fellows, machine learning engineers, biostatisticians, and medical students conducting studies using large-scale real-world and multimodal datasets. His work spans the full research workflow, from clinical question formulation to analysis and clinical interpretation. With more than 50 peer-reviewed journal articles and more than 100 peer-reviewed conference abstracts, Dr. Feng has established a research program that bridges cardiovascular science, advanced quantitative methodology, and translational decision support, with ongoing work focused on AF treatment optimization and dynamic cardiovascular risk prediction in multimodal data settings.