Clinical AI Systems
Interpretable prediction, clinical decision support, treatment recommendation, fairness, and deployment-aware healthcare analytics.
View programAI for healthcare impact
Building AI for real-world healthcare, clinical trials, and therapeutic science.
Sunlab, led by Prof. Jimeng Sun at UIUC, advances AI for clinical care, drug discovery, clinical trials, biosignals, and privacy-preserving health data.
Dr. Jimeng Sun
Health Innovation Professor, Siebel School of Computing and Data Science and Carle Illinois College of Medicine
Biography
Dr. Jimeng Sun
Dr. Jimeng Sun is the Health Innovation Professor in the Siebel School of Computing and Data Science and the Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign, and a cofounder of Keiji AI, a startup building AI for clinical trial intelligence and biomedical data science. Before joining UIUC, he was an associate professor in the College of Computing at Georgia Tech, where he co-directed the Center for Health Analytics and Informatics. His research advances artificial intelligence for healthcare across drug discovery, clinical trial optimization, computational phenotyping, clinical predictive modeling, treatment recommendation, and health monitoring. He earned his B.S. and M.Phil. in computer science from the Hong Kong University of Science and Technology and his Ph.D. in computer science from Carnegie Mellon University.
Research programs
Focused areas with clear paths to impact
Therapeutic Science
Foundation models, molecular optimization, drug-target interaction modeling, and open benchmarks for drug discovery.
View programClinical Trial Intelligence
Patient-trial matching, digital twins, trial outcome prediction, recruitment optimization, and trial design analytics.
View programSynthetic Data and Privacy
Privacy-preserving EHR and trial-data generation that enables broader research while respecting patient confidentiality.
View programMedical Foundation Models
Large language and multimodal models for medical reasoning, documentation, coding, literature mining, and clinical workflows.
View programBiosignals
Deep learning for EEG, sleep, seizure classification, cardiac monitoring, and cross-device physiological data analysis.
View programRepresentative work
Recognized research across healthcare AI and therapeutic science
- RETAIN: Interpretable predictive modeling for healthcare
- GraphCare: Personalized knowledge graphs for healthcare prediction
- Therapeutics Data Commons and AI foundations for therapeutic science
- BIOT: Transformer learning for real-world biosignal data
- Synthetic longitudinal EHR generation for privacy-preserving research
Software as a collaboration surface
PyHealth gives students and collaborators a concrete starting point for healthcare ML datasets, models, tasks, and reproducible experiments.
View PyHealthCollaboration
Clear next steps for students, postdocs, and collaborators
CS Students and Postdocs
The main recruiting path for computer science students and postdocs who want to build new machine learning methods, healthcare AI systems, and open-source research tools.
Clinical and Industry Collaborators
Clinicians, pharmaceutical scientists, health systems, pharma, biotech, and healthcare AI teams can bring real questions, data, and validation settings into rigorous AI research.