Jing Li

Associate Chair for Faculty Development and Research and
Virginia C. and Joseph C. Mello Chair and
Professor

Contact

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 404.894.6515
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  • Jing Li Google Scholar

Education

  • Ph.D. Industrial and Operations Engineering , University of Michigan
  • M.A. Statistics , University of Michigan
  • B.S. Civil Engineering , Tsinghua University

About

Jing Li is the Virginia C. and Joseph C. Mello Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and a core faculty in the Machine Learning Center at Georgia Tech. She is the Associate Chair for Faculty Development and Research in ISyE. Prior to joining Georgia Tech, she was a Professor at Arizona State University and is a co-founder of the ASU-Mayo Clinic Center for Innovative Imaging. She received her Ph.D. in Industrial and Operations Engineering and M.A. in Statistics from University of Michigan, and her B.S. from Tsinghua University.

Dr. Li is a former Chair for the Data Mining Subdivision of INFORMS. She is a Fellow of IISE, a recipient of the 2024 IISE/DAIS Professional Achievement Award, and a recipient of the 2025 Data Mining Prize from the Data Mining Subdivision of INFORMS. She is currently a Senior Editor for IEEE Transactions on Automation Science and Engineering and a Department Editor for IISE Transactions on Healthcare Systems Engineering. 

Research

Dr. Li’s research focuses on the development of machine learning (ML) and artificial intelligence (AI) methods for modeling and inference of complex-structured datasets characterized by high dimensionality (e.g., 3D/4D imaging, large graphs), multimodality, and heterogeneity. Her methodological development emphasizes the principled integration of domain knowledge—including mechanistic and simulation models, physical laws and constraints, and qualitative or descriptive knowledge—into the design of ML/AI models to improve accuracy, generalizability, and interpretability. The objectives of the methodological development are to provide capacities for monitoring & change detection, diagnosis, and prediction & prognosis. Her main application areas are in biomedical and healthcare domains, supporting research that spans fundamental discovery to personalized and precision medicine. Her research outcomes support clinical decision making for diagnosis, prognosis, and telemedicine for various conditions affecting the brain such as cancer, post-traumatic headache & migraine, brain injury, and the Alzheimer’s disease. In addition, her methodological development extends to other domains such as manufacturing quality control and precision agriculture. Her research received Best Paper awards from various professional venues such as IISE Transactions, IISE Annual Conferences, INFORMS Annual Conferences and Workshops, American Academy of Neurology, America Headache Society, etc. Her research has been funded by the NIH, NSF, DOD, USDA, and industries. She is an NSF CAREER Awardee. 

Representative Publications

  • Mao, L., Wang, H., Hu, S.L., Tran, N.L., Canoll, P.D., Swanson K.R., Li, J., 2025, “Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review.” IEEE Transactions on Automation Science and Engineering. 22:10008-10028.
  • Kwak, M. G., Mao, L., Zheng, Z., Su, Y., Lure, F., Li, J. 2025, “A Cross-Modal Mutual Knowledge Distillation Framework for Alzheimer’s Disease Diagnosis: Addressing Incomplete Modalities.” IEEE Transactions on Automation Science and Engineering. 22; 14219-14234.
  • Alenezi, D.F., Shi, J. and Li, J., 2025, “Graph-based Variation Propagation Network for Modeling and Prediction of Hybrid Multi-Stage Manufacturing Systems.” IEEE Transactions on Automation Science and Engineering. 22: 14318-14332.
  • Chopra, S., Sanchez-Rodriguez, G., Mao, L., Feola A.J., Li, J., Kira, Z. 2025, "MedMoE: Modality-Specialized Mixture of Experts for Medical Vision-Language Understanding." arXiv:2506.08356
  • Ku, D., Mao, L., Nikolova, S., Dumkrieger, G. M., Ross, K. B., Huentelman, M., ... Schwedt, T. J. 2025. “Longitudinal analysis of pain-induced brain activations in post-traumatic headache.” Cephalalgia, 45(5): 03331024251345160.
  • Lee, Y., Kwak, M.G., Chen, R., Yan, H., Mupparapu, M., Lure, F., Setzer, F., Li, J., 2025, “Oral-Anatomical Knowledge-Informed Semi-Supervised Learning for 3D Dental CBCT Segmentation and Lesion Detection.” IEEE Transactions on Automation Science and Engineering. 22: 11205-11218.
  • Wang, H., Argenziano, M.G., Yoon, H., ..., Li, J., 2024, "Biologically Informed Deep Neural Networks Provide Quantitative Assessment of Intratumoral Heterogeneity in Post Treatment Glioblastoma", Npj Digital Medicine - Nature, 7(1): 292.
  • Roy, K., Wang, B., Chen, R. Q., & Li, J., 2024, “Interfacing Data Science with Cell Therapy Manufacturing: Where We Are and Where We Need to be”, Cytotherapy, 26(8): 967-979.
  • Alenezi, D.F., Shi, J., Li, J., 2024, “Physics-informed Weakly-Supervised Learning for Quality Prediction of Manufacturing Processes”, IEEE Transactions on Automation Science and Engineering, 22: 2006-2019.
  • Zheng, Z., Su, Y., Chen, K., Weidman, D., Wu, T., Lo, S., Lure, F., Li, J. 2024, “Uncertainty-driven modality selection for data-efficient prediction of Alzheimer’s Disease.” IISE Transactions on Healthcare Systems Engineering. 14(1): 18-31.
  • Mao, L., Wang, L., …, Li, J., 2024, “Weakly-Supervised Transfer Learning with Application in Precision Medicine”, IEEE Transactions on Automation Science and Engineering, 21(4): 6250-6264.
  • Biehler, M., Sun, Y., Kode, S., Li, J., Shi, J., 2024. “PLURAL: 3D Point Cloud Transfer Learning via Contrastive Learning with Augmentations,” IEEE Transactions on Automation Science and Engineering. 21(4): 7550-7561.
  • Gaw, N., Li, J., Yoon, H., 2024, “A Novel Semi-supervised Learning Model for Smartphone-based Health Telemonitoring”, IEEE Transactions on Automation Science and Engineering, 21(1): 428-441.
  • Hu, L.,…, Li, J.,…, Tran, N., 2023, “Integrated Molecular and Multiparametric MRI Mapping of High-grade Glioma Identifies Regional Biologic Signatures”, Nature Communications, 14(1): 6066.
  • Chen, R.Q., Joffe, B., Costa, P.C., Filan, C., Wang, B., Balakirsky, S., Robles, F., Roy, K. and Li, J., 2023. “Real-time semantic segmentation and anomaly detection of functional images for cell therapy manufacturing.” Cytotherapy, 25(12): 1361-1369.
  • Wang, L., Schwedt, T., Chong, C., Wu, T., & Li, J., 2022, “Discriminant Subgraph Learning from Functional Brain Sensory Data” IISE Transactions, 54(11): 1084-1097.
  • Wang, L., Hawkins-Daarud, A., Swanson, K., Hu, L., & Li, J., 2021, “Knowledge-infused Global-Local Data Fusion for Spatial Predictive Modeling in Precision Medicine.” IEEE Transactions on Automation Science and Engineering, 19(3):2203-15.