Griffin Weber, M.D., Ph.D., is an Associate Professor of Medicine and Biomedical Informatics in the Department of Medicine, Beth Israel Deaconess Medical Center (BIDMC), and the Department of Biomedical Informatics, Harvard Medical School (HMS). He is also the Director of the Biomedical Research Informatics Core (BRIC) at BIDMC.
One of Dr. Weber's research areas is in expertise mining and social network analysis. He invented an open source social networking website for scientists called Profiles RNS (http://profiles.catalyst.harvard.edu). It automatically mines large datasets such as PubMed, NIH ExPORTER, and the U.S. patent database to discover investigators' research areas and scientific networks. It presents these connections using temporal, geospatial, and network visualizations. The software has numerous applications, ranging from finding individual collaborators and mentors to understanding the dynamics of an entire research community. Profiles RNS is now used at dozens of universities across the country.
Dr. Weber is also an investigator on Informatics for Integrating Biology and the Bedside (i2b2), an NIH National Center for Biomedical Computing, for which he helped developed a web-based open source platform that enables a variety of functions including queries of large clinical repositories for hypothesis testing and identification of patients for clinical trials (http://i2b2.org). He also created the original prototype software for the Shared Health Research Information Network (SHRINE), which is a federated query tool that connects i2b2 databases across multiple institutions. More than 100 institutions worldwide use i2b2 and SHRINE to support clinical research.
Dr. Weber received his M.D. and Ph.D. in computer science from Harvard University in 2007. While still a student, he became the first Chief Technology Officer of Harvard Medical School and built an educational web portal that provides interactive online content to over 500 courses. His past research projects also include analyzing DNA microarrays, modeling the growth of breast cancer tumors, and creating algorithms for predicting life expectancy.