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Li Shen, Ph.D., FAIMBE, FACMI, FAMIA
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Professor of Informatics in Biostatistics and Epidemiology
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Department: Biostatistics and Epidemiology
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Graduate Group Affiliations
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Department of Biostatistics, Epidemiology and Informatics
27 The Perelman School of Medicine
22 University of Pennsylvania
4d B306 Richards Building, 3700 Hamilton Walk
Philadelphia, PA 19104
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27 The Perelman School of Medicine
22 University of Pennsylvania
4d B306 Richards Building, 3700 Hamilton Walk
Philadelphia, PA 19104
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Office: 215-573-2956
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Publications
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Education:
21 7 BS 1d (Computer Science) c
33 Xi'an Jiao Tong University, 1993.
21 7 MS 1d (Computer Science) c
36 Shanghai Jiao Tong University, 1996.
21 8 PhD 1d (Computer Science) c
2a Dartmouth College, 2004.
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21 7 BS 1d (Computer Science) c
33 Xi'an Jiao Tong University, 1993.
21 7 MS 1d (Computer Science) c
36 Shanghai Jiao Tong University, 1996.
21 8 PhD 1d (Computer Science) c
2a Dartmouth College, 2004.
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Links
be Search PubMed for articles
5b Shen Lab Page
70 DBEI Faculty Page
7d BGS Faculty Page
79 PSOM Faculty Page
65 SEAS Faculty Page
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Permanent linkbe Search PubMed for articles
5b Shen Lab Page
70 DBEI Faculty Page
7d BGS Faculty Page
79 PSOM Faculty Page
65 SEAS Faculty Page
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396 Dr. Li 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 450 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.
2e5 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, biomedical and health informatics, and computational biology. 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.
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18 Keywords:
12b 1. Foundational AI, Machine Learning & Computer Science: Machine learning; Trustworthy, explainable, and responsible AI; Generative and agentic AI; Multimodal AI; Natural language processing (NLP) and large language models (LLMs); Computer Vision; Computational Geometry; Data Mining.
d8 2. Biomedical & Health Informatics: Translational bioinformatics; Clinical research informatics; Health Informatics; Radiology and imaging informatics; Biomedical signal, sensor and EHR data analytics.
c0 3. Genomics, Multi-omics, Computational Biology & Neuroscience: Genetics and multi-omics; Single cell and/or spatial omics; Computational biology; Systems biology; Neuroscience.
a2 4. Drug Discovery & Therapeutic Innovation: Drug repurposing; Drug discovery; AI-accelerated therapeutic design; Drug Adherence; Pharmacovigilance.
c4 5. Disease-focused Areas: Early detection, treatment, & prevention of complex diseases (e.g., dementia, immune disorders); Precision medicine; Risk prediction; Staging & subtyping.
77 6. Human Health, Care Delivery & Support: Caregiving support; Digital health; Clinical decision support.
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36 Research Details and Rotation Projects:
7a See https://www.med.upenn.edu/shenlab/research.html
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1d Lab Personnel:
7e See https://www.med.upenn.edu/shenlab/lab-members.html
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Description of Research Expertise
2b Research Interests:396 Dr. Li 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 450 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.
2e5 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, biomedical and health informatics, and computational biology. 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.
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18 Keywords:
12b 1. Foundational AI, Machine Learning & Computer Science: Machine learning; Trustworthy, explainable, and responsible AI; Generative and agentic AI; Multimodal AI; Natural language processing (NLP) and large language models (LLMs); Computer Vision; Computational Geometry; Data Mining.
d8 2. Biomedical & Health Informatics: Translational bioinformatics; Clinical research informatics; Health Informatics; Radiology and imaging informatics; Biomedical signal, sensor and EHR data analytics.
c0 3. Genomics, Multi-omics, Computational Biology & Neuroscience: Genetics and multi-omics; Single cell and/or spatial omics; Computational biology; Systems biology; Neuroscience.
a2 4. Drug Discovery & Therapeutic Innovation: Drug repurposing; Drug discovery; AI-accelerated therapeutic design; Drug Adherence; Pharmacovigilance.
c4 5. Disease-focused Areas: Early detection, treatment, & prevention of complex diseases (e.g., dementia, immune disorders); Precision medicine; Risk prediction; Staging & subtyping.
77 6. Human Health, Care Delivery & Support: Caregiving support; Digital health; Clinical decision support.
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36 Research Details and Rotation Projects:
7a See https://www.med.upenn.edu/shenlab/research.html
8
1d Lab Personnel:
7e See https://www.med.upenn.edu/shenlab/lab-members.html
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143 Zhan Q, Zhou Z, Wang Z, Long Q, Shen L: Bi-Lipschitz Autoencoder With Injectivity Guarantee. ICLR’26: The International Conference on Learning Representations 2026 Notes: [28% acceptance rate] Proceeding represents original peer-reviewed research.
d7 Jin R, Tong B, Yang S, Hou B, Shen L, for Alzheimer’s Disease Neuroimaging Initiative: ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning 2 5d IEEE Trans Artif Intell. doi: 10.1109/tai.2025.3647596. Dec 23 2025.
187 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.
187 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.
146 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.
110 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.
12b 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.
171 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.
157 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.
217 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.
163 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.
100 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
196 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.
11a 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.
f2 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.
180 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.
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Selected Publications
127 Wang Z, Zhan Q, Yang S, Zhou Z, Kan M, Zhai T, Shen L: An Interpretable Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis. Gigascience. doi: 10.1093/gigascience/giag012 Feb 9 2026.143 Zhan Q, Zhou Z, Wang Z, Long Q, Shen L: Bi-Lipschitz Autoencoder With Injectivity Guarantee. ICLR’26: The International Conference on Learning Representations 2026 Notes: [28% acceptance rate] Proceeding represents original peer-reviewed research.
d7 Jin R, Tong B, Yang S, Hou B, Shen L, for Alzheimer’s Disease Neuroimaging Initiative: ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning 2 5d IEEE Trans Artif Intell. doi: 10.1109/tai.2025.3647596. Dec 23 2025.
187 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.
187 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.
146 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.
110 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.
12b 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.
171 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.
157 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.
217 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.
163 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.
100 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
196 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.
11a 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.
f2 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.
180 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.
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