Vasant Honavar
CTSI Informatics Core Co-Lead
Penn State University
Dr. Honavar is currently a Professor of Information Sciences and Technology, Bioinformatics and Genomics, and of Neuroscience, at Pennsylvania State University where he holds the Dorothy Foehr Huck and J. Lloyd Huck Chair in Biomedical Data Sciences and Artificial Intelligence. He is the founding director of the Center for Artificial Intelligence Foundations and Scientific Applications, and Associate Director for the Institute for Computational and Data Science and roles in which he provides leadership for the university’s research and training initiatives in Artificial Intelligence, Machine Learning, and Data Sciences. He has a broad background in Computer Science and Informatics, with specific expertise in machine learning, knowledge representation, information integration, causal inference, big data analytics, and bioinformatics and health informatics.
His work (documented in over 300 peer-reviewed publications, with over 19,000 citations, h-index=63) has resulted in foundational contributions in scalable methods for learning predictive models from (distributed, heterogeneous, multi-modal, longitudinal, and ultrahigh-dimensional) big data; Deep learning methods for representation learning from complex data; Eliciting causal effects from observational and experimental data; Selective sharing of knowledge across disparate knowledge bases; and applications in bioinformatics (characterization and prediction of protein-protein, protein-RNA interactions, interfaces, and complexes; prediction of B-cell and T-cell epitopes; eliciting disease biomarkers from multi-omics data) and health informatics (predictive and causal modeling of health risks and health outcomes from clinical (EHR), socio-demographic, and behavioral data). This research has resulted in some widely used software, webservers, and tools.
In his role as Informatics Co-lead of the NIH NCATS funded Clinical and Translational Sciences Institute at Penn State, he leads the development of Digital Collaboratory for Precision Health Research for data access and use policy compliant reproducible, auditable, secure, integrative analyses of health data (including EHR and socio-demographic data; and the applications of the resulting infrastructure and state-of-the-art machine learning and causal inference tools for predictive and causal modeling of health risks, health outcomes, and health disparities. He has significant experience in developing interdisciplinary curricula at the interface between artificial intelligence and biomedical, life and health sciences (in his role as founding faculty of the Data Sciences program at Pennsylvania State University and Bioinformatics and Genomics graduate program at Iowa State University).
