معرفی
Jie Ding is an Associate Professor in the Department of Statistics at the University of Minnesota, College of Science and Engineering. His research bridges statistical theory and artificial intelligence, with a focus on federated learning, continual learning, and AI applications in biomedicine and telecommunications.
Research Interests: Jie Ding's work centers on developing robust and scalable machine learning systems. His interests include federated and collaborative learning frameworks, statistical modeling under data scarcity, and AI-driven solutions for biomedical engineering and 5G network optimization. He investigates foundational aspects of model selection, information fusion, and adversarial robustness.
The recent publications highlight a strong trend in distributed and secure AI, particularly in federated learning with dynamic resource allocation, backdoor attack analysis, and AI applications in cardiac organoid maturation. His work integrates statistical rigor with real-world deployment challenges across heterogeneous systems.
Scientific Awards:
- No awards explicitly listed in the provided text.
Advising and Grants: Dr. Ding serves as Principal Investigator (PI) on multiple major research grants, including an NSF CAREER award on continual learning and projects funded by the U.S. Department of Defense and NIH. These projects involve collaborative research with Harvard University and focus on AI-driven flexible electronics, information fusion, and 5G measurement modeling. While specific student names are not listed, his leadership of large-scale research initiatives implies active graduate student mentoring.
Labs and Teams: Dr. Ding leads a research group focused on AI and statistical learning, collaborating with interdisciplinary teams in engineering, computer science, and biomedical research. His projects involve partnerships with Harvard University and the National Institutes of Health, indicating a strong network in both academic and applied research domains.
