Naveena Yanamala, PhD

Section Chief of Clinical Research and AI Innovation

Director of Data Science and Machine Learning

Dr. Naveena Yanamala, MS, PhD, is an Associate Professor of Medicine, Section Chief of Clinical Research & AI Innovation, and Director of Data Science and Machine Learning Research in the Division of Cardiology at Rutgers Robert Wood Johnson Medical School in New Brunswick, NJ. Additionally, she directs the Center for Innovation at RWJUH, serves as an Editorial Board Member for the American Society of Echocardiography (ASE), and holds roles as Associate Editor for Translational Systems Biology and In Silico Trials.

She has held several leadership positions in occupational safety and health research governance, including serving as a steering committee member of the Center for Occupational Robotics Research (CORR) within the Division of Safety Research. Additionally, she has acted as a liaison for the Emerging Technologies Interest Group (ETIG), focusing on the integration of machine learning and artificial intelligence across CDC/NIOSH. She was recently appointed as a Special Government Employee on the Patient Engagement Advisory Committee for the Center for Devices and Radiological Health (CDRH) at the US FDA. She also holds academic affiliations as an Associate Member of the Biomedical Engineering (BME) Graduate Program at Rutgers University and as an Adjunct Faculty member at Carnegie Mellon University.

With over 15 years of interdisciplinary research experience, Dr. Yanamala has authored more than 90 peer-reviewed articles at the intersection of biology, health, and computation. Her current research focuses on advancing science and healthcare through applied ML/AI methodologies, with an emphasis on developing AI-driven solutions. This includes algorithms that leverage wearables and point-of-care technologies to emulate diagnostic medical imaging, enhancing accessibility and reducing costs. She is also pioneering efforts to improve healthcare accessibility through robotics and other innovative medical technologies, ensuring that advanced diagnostic tools reach diverse and underserved patient populations. By harnessing data-driven innovations, her work bridges gaps in health equity and public health, transforming the landscape of preventive and precision medicine.

Some of her notable recognitions include winning the NIH NHLBI Big Data Analysis Challenge in 2020 and multiple Alice Hamilton Awards, which honor exceptional scientific contributions to occupational and environmental health. Additionally, she has received nominations for the prestigious Charles C. Shepard Science Award, the CDC’s highest recognition for excellence in science and research.

Dooley SW, Yanamala NVK, Al-Roub N, Spetko N, Cassidy MA, Angell-James C, Sengupta PP, Strom JB. Machine Learning to Stratify Risk in Low-Gradient Aortic Stenosis Among Medicare Beneficiaries. J Am Soc Echocardiogr. 2024 Oct 30:S0894-7317(24)00528-5. doi: 10.1016/j.echo.2024.10.010. Epub ahead of print. PMID: 39481666.

Duenas S, McGee Z, Mhatre I, Mayilvahanan K, Patel KK, Abdelhalim H, Jayprakash A, Wasif U, Nwankwo O, Degroat W, Yanamala N, Sengupta PP, Fine D, Ahmed Z. Computational approaches to investigate the relationship between periodontitis and cardiovascular diseases for precision medicine. Hum Genomics. 2024 Oct 19;18(1):116. doi: 10.1186/s40246-024-00685-7. PMID: 39427205; PMCID: PMC11491019.

Sengupta PP, Dey D, Davies RH, Duchateau N, Yanamala N. Challenges for augmenting intelligence in cardiac imaging. Lancet Digit Health. 2024 Oct;6(10):e739-e748. doi: 10.1016/S2589-7500(24)00142-0. Epub 2024 Aug 29. PMID: 39214759.