About Me
I am currently a Postdoctoral Research Fellow working with Professor Tianxi Cai at the Translational Data Science Center for a Learning Health System (CELEHS), Harvard Medical School. I also serve as a data scientist at the Veterans Affairs Boston Healthcare System, collaborating with Dr. Kelly Cho. I received my Ph.D. in Computer Science from Zhejiang University in 2020. In January 2026, I will join the Department of Computational Biology at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) as an Assistant Professor, where I am founding the PAI Lab (Precision-medicine AI Lab). Our mission is to advance artificial intelligence for precision medicine by integrating multi-scale biomedical data to improve diagnosis, treatment, and drug discovery. We are actively recruiting postdoctoral fellows, Ph.D. students, master’s students, and visiting undergraduate researchers who are passionate about AI, computational biology, and translational biomedicine. If you are motivated to work at the intersection of AI and precision medicine, we warmly welcome you to join our growing team at PAI_Lab@MBZUAI.
Research Interest
Our research focuses on developing AI-driven frameworks, particularly network-based AI models, to advance precision medicine by integrating multimodal biomedical data, including biomedical knowledge graphs, electronic health records (EHRs), foundational models, large-scale Biobank data, etc. We aim to understand the complex interplays among medications, genetic variants, and diseases (or phenotypes), specifically
- Understand the consequences of genetic variants on clinical phenotypes, drug's therapeutic or adverse effects (e.g., PheMART)
- Discover drug-gene-disease relationships for drug development, repurposing, and safety (e.g., INTERLACE, HERMES, CaSBRE)
- EHR-based translational research, including incident phenotyping, disease risk prediction, rare disease diagnosing, and data-driven medical knowledge graph (e.g., LATTE, SeDDLeR, DOME)
Selected Publications
Phenotypic Prediction of Missense Variants via Deep Contrastive Learning
J. Wen, S. Zeng, ..., J. S. Liu, T. Cai
Nature Biomedical Engeering, accepted, 2025
LATTE: Label-efficient Incident Phenotyping from Longitudinal EHR
J. Wen, J. Hou, C. Bonzel, Y. Zhao, ..., T. Cai
Patterns (Cell), 5(1), Cover Article, 2024
Multimodal Representation Learning for Predicting Molecule–Disease Relations
J. Wen, X. Zhang, E. Rush, ..., T. Cai
Bioinformatics, 39(2), btad085, 2023
DOME: Directional Medical Embeddings from EHR
J. Wen*, H. Xue*, E. Rush, ..., T. Cai
Journal of Biomedical Informatics, 104768, 2025
Integrating Knowledge Graph and Electronic Health Records for Drug Repurposing
J. Wen, N. Zhou, S., Cai ..., T. Cai
under submision, 2025
HOVER: Hyperbolic Video-Text Retrieval
J. Wen*, Y. Chen*, ..., R. Zimmermann
IEEE Transactions on Image Processing, accepted, 2025
Label-efficient Phenotyping for Long COVID Using EHR
C. Hong*, J. Wen*, H. Zhang*, ..., T. Cai
npj Digital Medicine, 8(1), 405, 2025
Heterogeneous Entity Representation for Medicinal Synergy Prediction
J. Wu*, J. Wen*, M. Yan*, ..., C. Chen
Bioinformatics, 41(1), btae750, 2025
Deep Learning from EHR to Identify RCC Recurrence
J. Hou*, J. Wen*, R. Bhattacharya, ..., T. Cai
Annals of Oncology, 35, S1027, 2024
SeDDLeR: Semi-supervised Double Deep Learning for Temporal Risk Prediction
I. E. Nogues*, J. Wen*, Y. Zhao, ..., T. Cai
Journal of Biomedical Informatics, 157, 104685, 2024
Weakly Semi-supervised Phenotyping Using EHR
I. E. Nogues, J. Wen, Y. Lin, ..., T. Cai, C. Hong
Journal of Biomedical Informatics, 134, 104175, 2022
SAMGEP: A Semi-supervised Adaptive Markov Gaussian Embedding Process for Phenotype Event Prediction
Y. Ahuja*, J. Wen*, ..., T. Cai
Scientific Reports, 12(1), 17737, 2022
CaSBRE: Causality-inspired Semi-supervised Biomedical Relation Extraction
S. Zeng*, J. Wen*, J. Du, ..., H. Wang
Under revision, 2025
Bayesian Uncertainty Matching for Unsupervised Domain Adaptation
J. Wen, N. Zheng, J. Yuan, Z. Gong, C. Chen
International Joint Conferences on Artificial Intelligenc (IJCAI), 2019
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
J. Wen, R. Liu, N. Zheng, Q. Zheng, Z. Gong, J. Yuan
AAAI Conference on Artificial Intelligence (AAAI), 2019
[PDF]
Unsupervised Representation Learning with Long-Term Dynamics for Skeleton-Based Action Recognition
J. Wen, N. Zheng, R. Liu, L. Long, J. Dai, Z. Gong
AAAI Conference on Artificial Intelligence (AAAI), 2018
[PDF]
Discriminative radial domain adaptation
Z. Huang, J. Wen, S. Chen, ..., N. Zheng
IEEE Transactions on Image Processing, 32, 1419-1431, 2023
Contrast-Reconstruction Representation Learning for Self-supervised Skeleton-Based Action Recognition
P. Wang, J. Wen, C. Si, ..., L. Wang
IEEE Transactions on Image Processing, 31, 6224–6238, 2022
Towards More General Loss and Setting in Unsupervised Domain Adaptation
C. Shui, R. Pu, G. Xu, J. Wen, ..., B. Wang
IEEE Transactions on Knowledge and Data Engineering, 35(10), 10140–10150, 2023
Generating Analysis-Ready Data for Real-World Evidence from EHR
J. Hou, R. Zhao, J. Gronsbell, ..., J. Wen, ..., T. Cai
Journal of Medical Internet Research, 25, e45662, 2023
A Two-layer Neural Circuit Controls Fast Forward Locomotion in Drosophila
Q. Zhao, X. Li, J. Wen, Y. He, ..., Z. Gong
Current Biology, 34(15), 3439–3453, 2024