Li Shen, Ph.D., FAIMBE, FACMI, FAMIA
Professor of Informatics in Biostatistics and Epidemiology
Department: Biostatistics and Epidemiology
Graduate Group Affiliations
Contact information
Department of Biostatistics, Epidemiology and Informatics
The Perelman School of Medicine
University of Pennsylvania
B306 Richards Building, 3700 Hamilton Walk
Philadelphia, PA 19104
The Perelman School of Medicine
University of Pennsylvania
B306 Richards Building, 3700 Hamilton Walk
Philadelphia, PA 19104
Office: 215-573-2956
Publications
Education:
BS (Computer Science)
Xi'an Jiao Tong University, 1993.
MS (Computer Science)
Shanghai Jiao Tong University, 1996.
PhD (Computer Science)
Dartmouth College, 2004.
Permanent linkBS (Computer Science)
Xi'an Jiao Tong University, 1993.
MS (Computer Science)
Shanghai Jiao Tong University, 1996.
PhD (Computer Science)
Dartmouth College, 2004.
Description of Research Expertise
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 secondary appointments in the Department of Radiology and the Department of Computer and Information Science. 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 is a pioneer in bioinformatics strategies for brain-wide genome-wide association studies to advance Alzheimer’s disease research. His research spans artificial intelligence (AI), machine learning (ML), biomedical and health informatics, NLP/LLMs, medical image computing, network science, and multi-omics and systems biology, with applications across complex disorders. He has authored over 430 peer-reviewed articles in these fields. His work has been continuously supported by the NIH and NSF. His primary focus is on developing and applying advanced AI/ML/Informatics methods to analyze large-scale biobank and health datasets, aiming to improve understanding, early detection, treatment, prevention, and overall healthcare of complex disorders. He also explores emerging frontiers such as generative AI, agentic AI, and trustworthy multimodal AI to push the boundaries of biomedical research.
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 fellow of the American College of Medical Informatics (ACMI), a fellow of the American Medical Informatics Association (AMIA), a distinguished member of the Association for Computing Machinery (ACM), and a distinguished contributor of the IEEE Computer Society.
Selected Publications
Bao J, Wen J, Chang C, Mu S, Chen J, Shivakumar M, Cui Y, Erus G, Yang Z, Yang S, Wen Z; Alzheimer’s Disease Neuroimaging Initiative; Zhao Y, Kim D, Duong-Tran D, Saykin AJ, Zhao B, Davatzikos C, Long Q, Shen L.: A genetically informed brain atlas for enhancing brain imaging genomics. Nat Commun 16: 3524, Apr 2025.Chen J, Ionita M, Feng Y, Lu Y, Orzechowski P, Garai S, Hassinger K, Bao J, Wen J, Duong-Tran D, Wagenaar J, McKeague ML, Painter MM, Mathew D, Pattekar A, Meyer NJ, Wherry EJ, Greenplate AR, Shen L.: Automated cytometric gating with human-level performance using bivariate segmentation. Nat Commun 16: 1576, Feb 2025.
Xu J, Wei T, Hou B, Orzechowski P, Yang S, Jin R, Paulbeck R, Wagenaar J, Demiris G, Shen L.: MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance. KDD'25: 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2025.
Jin R, Hou B, Xiao J, Su WJ, Shen L: Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic. ICLR’25: The International Conference on Learning Representations 2025.
Xiao J, Hou B, Wang Z, Jin R, Long Q, Su WJ, Shen L: Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach. ICML’25: Forty-second International Conference on Machine Learning 2025.
Hou B, Wen Z, Bao J, Zhang R, Tong B, Yang S, Wen J, Cui Y, Moore JH, Saykin AJ, Huang H, Thompson PM, Ritchie MD, Davatzikos C, Shen L; Alzheimer’s Disease Neuroimaging Initiative.: Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery. Med Image Anal 2024.
Li D, Yang S, Tan Z, Baik JY, Yun S, Lee J, Chacko A, Hou B, Duong-Tran D, Ding Y, Liu H*, Shen L*, Chen T*: DALK: Dynamic co-augmentation of LLMs and KG to answer Alzheimer's disease questions with scientific literature. EMNLP’24: The 2024 Conference on Empirical Methods in Natural Language Processing 2024.
Zhou Z, Tarzanagh DA, Hou B, Long Q, Shen L.: Fairness-Aware Estimation of Graphical Models. NeurIPS’24: 38th Conference on Neural Information Processing Systems 2024.
Wang Z, Zhan Q, Yang S, Mu S, Chen J, Garai S, Orzechowski P, Wagenaar J, Shen L.: QOT: Quantized Optimal Transport for sample-level distance matrix in single-cell omics. Brief Bioinform 26: bbae713, Nov 2024.
Yan Jingwen, Du Lei, Kim Sungeun, Risacher Shannon L, Huang Heng, Moore Jason H, Saykin Andrew J, Shen Li: Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm. Bioinformatics (Oxford, England) 30(17): i564-71, Sep 2014.
Shen Li, Kim Sungeun, Risacher Shannon L, Nho Kwangsik, Swaminathan Shanker, West John D, Foroud Tatiana, Pankratz Nathan, Moore Jason H, Sloan Chantel D, Huentelman Matthew J, Craig David W, Dechairo Bryan M, Potkin Steven G, Jack Clifford R, Weiner Michael W, Saykin Andrew J: Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. NeuroImage 53(3): 1051-63, Nov 2010.
Yao Xiaohui, Risacher Shannon L, Nho Kwangsik, Saykin Andrew J, Wang Ze, Shen Li: Targeted genetic analysis of cerebral blood flow imaging phenotypes implicates the INPP5D gene. Neurobiology of aging 81: 213-221, Sep 2019 Notes: https://doi.org/10.1016/j.neurobiolaging.2019.06.003.
Shen L, Thompson PM: Brain imaging genomics: integrated analysis and machine learning. Proceedings of the IEEE 108(1): 125-162, 2020 Notes: https://doi.org/10.1109/JPROC.2019.2947272
Bao, J., Wen, J., Wen, Z., Yang, S., Cui, Y., Yang, Z., Erus, G., Saykin, A. J., Long, Q., Davatzikos, C., Shen, L.: Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer''s disease. Neuroimage 280: 120346, 2023.
Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Jia Xu, Yanbo Feng, Qi Long, Li Shen: Fair Canonical Correlation Analysis. NeurIPS’23: 37th Conference on Neural Information Processing Systems Page: https://openreview.net/forum?id=W3cDd5xlKZ, 2023 Notes: Proceeding represents original peer-reviewed research.