Stanford University
Showing 33,811-33,820 of 37,009 Results
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Chen Wang, MD, PhD
Clinical Assistant Professor, Dermatology
BioDr. Wang is a board-certified dermatologist with the Stanford Dermatology Clinic and a clinical assistant professor in the Department of Dermatology at Stanford University School of Medicine.
Dr. Wang specializes in medical dermatology. He commonly diagnoses and treats conditions such as acne, inflammatory skin diseases, hair loss, and skin cancer.
Dr. Wang completed a postdoctoral fellowship in immunology research with the Mark M. Davis Laboratory at Stanford University. His research interests include cutaneous immunology and the role of T cells in inflammatory skin diseases. -
C. Jason Wang, MD, PhD
LCY: Tan Lan Lee Professor and Professor of Pediatrics (General Pediatrics) and of Health Policy
BioDr. Wang is the Director of Center for Policy, Outcomes and Prevention. Prior to coming to Stanford in 2011, he was a faculty member at Boston University Schools of Medicine and Public Health. His other professional experiences include working as a management consultant with McKinsey and Company and serving as the project manager for Taiwan's National Health Insurance Reform Task-force. His current interests include: 1) pandemic preparedness; 2) role of generative Ai on child health and development; 3) use of mobile technology in improving quality of care; 4) assessing and improving the value of healthcare, and 5) healthcare delivery innovations and payment reforms.
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Chong Wang
Postdoctoral Scholar, Radiology
BioI am currently a Postdoctoral Scholar in the Department of Radiology at Stanford University School of Medicine, affiliated with the Center for Artificial Intelligence in Medicine and Imaging (AIMI). My research mainly focuses on AI and foundation models in healthcare, with an emphasis on developing trustworthy, robust, and efficient AI solutions for medical imaging. I earned my Ph.D degree, honored with the Doctoral Research Medal, in Computer Science from the Australian Institute for Machine Learning (AIML), The University of Adelaide.
