Dr. Naveena Yanamala

Advisor and Chair,
Student Experience Committee

Dr. Yanamala, a passionate educator in relentless pursuit of the advancement of applied learning methods in AI presently serves as the Principal Data Scientist in the Heart & Vascular Institute at WVU Medicine. She holds additional appointments as an Adjunct Professor in the Institute for Software Research, School of Computer Science at the Carnegie Mellon University, USA and in the Center for Computational Natural Sciences and Biology, International Institute for Information Technology (IIIT). A highly accomplished and sought-after scientist, she has over 70+ journal articles, review articles and conference papers published in international and national scientific journals. Besides, research and teaching AI & ML to a multitude of college level students, she is an ardent advocate of science, technology, engineering, and mathematics (STEM) initiatives and has inspired several students at the elementary/middle/high school levels to pursue their dreams in the space of robotics and artificial intelligence.

Following her PhD and her work at the University of Pittsburgh, USA she has contributed to significant research from 2012 to 2020, as a Research Biologist (Service Fellow) in the Health Effects Laboratory Division at CDC/NIOSH, Morgantown. During this stint, Dr. Yanamala also served as a steering committee member of center for occupational robotics research (CORR) and as liaison for machine learning and artificial intelligence in a CDC/NIOSH wide emerging technologies interest group (ETIG), while maintaining an active collaboration with various institutions, including University of Pittsburgh, West Virginia University Health Sciences Center, Carnegie Mellon University and several different research teams across NIOSH with a major focus on applied AI. Dr. Yanamala currently serves on the Editorial Board of International Journal of Nanomaterials, Nanotechnology and Nanomedicine.
Her research has led to groundbreaking work recorded in at least two impactful publications drawing 100s of citations. Some of her notable work towards the nanotoxicology research identified sensitive markers that can predict possible toxicological effects and distinguish exposure to different nanomaterials, improving the occupational/consumers safety. Especially her contributions to biodegradation of nanomaterials changed the long-standing belief of the scientific community that carbon nanotubes do not break down in the body or nature. More recently, her work has focused on developing conceptually novel approaches for understanding mechanisms that drive cardiovascular health and function. Specifically, she has applied AI/ML techniques to pioneer approaches in left ventricular pattern recognition, dynamic remodeling of the left ventricle, and cardiovascular health diagnostics. A recent pilot study on the prediction of coronary artery calcium scoring from electrocardiogram (ECG) in atherosclerotic cardiovascular disease has paved the way for early detection of heart disease in outpatient settings. These are just few of the many important works to her credit.

Dr. Yanamala received her Ph.D. in Integrative Systems Biology in 2009 from University of Pittsburgh and her MS in Information Technology with specialization in Bioinformatics, from International Institute of Information Technology, Hyderabad, India along with an internship in ML from Carnegie Mellon University in 2004. She possesses a Bachelor of Science degree from S. V. University in Computer Science. Dr. Yanamala has 14+ years of experience in conducting effective interdisciplinary research at the intersection of biology, health, and computation. As an applied ML/AI researcher, her research efforts have focused on issues as varied as (a) identifying factors that mitigate toxicity to enable safe-design of novel engineered materials, (b) developing biomarkers of exposure/disease, to classify or predict disease outcomes in environmental/occupational settings, (c) automation of medical diagnostic processes and healthcare forecasting (e.g., pathology, radiology, scheduling), (d) cardiovascular pathophysiology and (e) scalable solutions for intelligent automation in public health surveillance. Most of her research has been at the heart of such endeavors ranging from providing improved insights through to automation, combining informatics, data analytics, computational biology, toxicology, and medical sciences.