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 PyHealthAudience pathways
Clear next steps for the people Sunlab wants to attract
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 Partners
Clinicians, pharmaceutical scientists, medical students, residents, and fellows can bring real clinical questions into AI projects with publishable and translational impact.
Industry Collaborators
Partner on clinically grounded AI for trials, real-world evidence, drug discovery, safety, operations, and deployable healthcare ML systems.
Investors and Venture Partners
Understand the lab's translation engine: research excellence, software assets, clinical partnerships, and startup formation in AI-enabled healthcare.
Collaboration fit
Good projects connect data, domain expertise, and a real deployment or decision point.
Sunlab is especially well positioned for problems involving health data complexity, trustworthy modeling, clinical validation, and the bridge from prototypes to usable systems.
Clinicians and clinical departmentsMedical students, residents, and fellowsHospitals and health systemsPharma and biotechClinical research organizationsHealth AI startups
Contact
Let us build practical AI solutions for high-impact healthcare challenges.
For prospective students, include your CV, research interests, coding samples, and any PyHealth or open-source contributions. For clinical partners, collaborators, and investors, describe the opportunity, timeline, data access, and desired outcome.