Li Shen, PhD, FAIMBE
Professor of Informatics
Dr. Li Shen is a Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University of Pennsylvania. He also holds a secondary appointment in the Department of Radiology. He is a Senior Fellow at the Penn Institute for Biomedical Informatics and the Leonard Davis Institute of Health Economics. He obtained his Ph.D. degree in Computer Science from Dartmouth College.
Dr. Shen's research interests include medical image computing, biomedical informatics, machine learning, network science, imaging genomics, multi-omics and systems biology, Alzheimer’s disease, and big data science in biomedicine. He has authored over 300 peer-reviewed articles in these fields. His work has been continuously supported by the NIH and NSF. His current research program is focused on developing and applying informatics, computing and data science methods for discovering actionable knowledge from complex biomedical and health data (e.g., genetics, omics, imaging, biomarker, outcome, EHR, health care), with applications to complex disorders such as Alzheimer’s disease.
Dr. Shen has served on a variety of scientific journal editorial boards, grant review committees, and organizing committees of professional meetings in medical image computing and biomedical informatics. He served as the Executive Director of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society between 2016 and 2019. He is a fellow of the American Institute for Medical and Biological Engineering (AIMBE), a distinguished member of the Association for Computing Machinery (ACM), and a distinguished contributor of the IEEE Computer Society.
Content Area Specialties
Imaging genomics, biomedical imaging sciences, multi-omics and systems biology, biomarker discovery, drug study, electronic health records, brain disorders, and Alzheimer's disease.
Medical image computing, bioinformatics, machine learning, network science, visual analytics, shape analysis, and big data science in biomedicine.