Li Shen, PhD, FAIMBE



Li Shen, PhD, FAIMBE

Professor of Informatics

Dr. Shen is a Professor and the Deputy Director of the Informatics Division in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University of Pennsylvania. He holds secondary appointment in Department of Radiology, and graduate group appointments in AMCS, Bioengineering, GGEB, GCB, and Neuroscience. He is a Senior Fellow at the Institute for Biomedical Informatics (IBI) and the Leonard Davis Institute of Health Economics. He serves as Associate Director for Bioinformatics at the IBI,  Faculty Director of the IBI Bioinformatics Core, and Co-Director of the AI2D Center.

Dr. Shen obtained his Ph.D. degree in Computer Science from Dartmouth College. His research interests include medical image computing, biomedical informatics, machine learning, trustworthy AI, NLP/LLMs, network science, imaging genomics, multi-omics and systems biology, Alzheimer’s disease, and big data science in biomedicine. 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 MICCAI Society (2016-2019). He is a fellow of the American Institute for Medical and Biological Engineering, a distinguished member of the Association for Computing Machinery, 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.

Methodology Specialties

Medical image computing, bioinformatics, machine learning, network science, visual analytics, shape analysis, and big data science in biomedicine.


About Us

To understand health and disease today, we need new thinking and novel science —the kind  we create when multiple disciplines work together from the ground up. That is why this department has put forward a bold vision in population-health science: a single academic home for biostatistics, epidemiology and informatics. 

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