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Nina Li

Community Program Manager, Stanford CARE, Medicine - Med/Family and Community Medicine

Current Role at StanfordCommunity Program Manager, Stanford CARE

Contact Info
- 3180 Porter Dr
- Palo Alto, California 94304-1212
Mail Code: 5411
- (650) 721-6177 (office)
ln1102@stanford.edu

BioRon Li is a Clinical Associate Professor of Medicine in the Division of Hospital Medicine and Center for Biomedical Informatics Research at Stanford University School of Medicine. As the Medical Informatics Director for Digital Health at Stanford Health Care, he provides medical and informatics direction for the health system's enterprise digital health portfolio, including expanding digital referral networks and virtual care modalities. He is the co-founder and Director for the Stanford Emerging Applications Lab (SEAL), which helps clinicians and staff build ideas into novel digital products that are prototyped and tested for care delivery at Stanford Health Care.

Ron's academic interests focus on the "delivery science" of new technological capabilities such as digital and artificial intelligence in healthcare and how to design, implement, and evaluate new tech enabled models of care delivery. Ron's work spans across multiple disciplines, including clinical medicine, data science, digital health, information technology, design thinking, process improvement, and implementation science. He has consulted for various companies in the digital health and artificial intelligence space. He is an attending physician on the inpatient medicine teaching service at Stanford Hospital and is the Associate Program Director for the Stanford Clinical Informatics Fellowship.

Contact Info
Mail Code: 5475
ronl@stanford.edu

Rufei Li

Postdoctoral Scholar, Neurobiology

Contact Info
Mail Code: 5125
rufeili@stanford.edu

Ruijiang Li

Associate Professor (Research) of Radiation Oncology (Radiation Physics)

Current Research and Scholarly InterestsMy lab's research is focused on the development of imaging and molecular biomarkers to improve cancer detection, diagnosis, prognostication, and prediction of therapy response. Our ultimate goal is to translate these biomarkers into clinical practice to guide optimal management and therapeutic decisions for precision cancer medicine.