Dr. Ruijiang Li is a distinguished researcher and educator at the forefront of medical physics and radiation oncology at Stanford University. He currently serves as Associate Professor of Radiation Oncology (Radiation Physics) at Stanford University School of Medicine, with additional affiliations as a Member of Bio-X and the Stanford Cancer Institute. Dr. Li earned his Ph.D. in Electrical and Computer Engineering from the University of Florida in 2008, where his doctoral research focused on advanced signal processing methods for human electroencephalography analysis. Following his doctoral studies, he completed postdoctoral training at UCSD in medical physics and radiation therapy, specializing in motion management techniques for lung cancer radiotherapy before joining Stanford in 2011.
Dr. Li's pioneering research program has generated substantial scholarly impact with over 9,975 citations according to Google Scholar, reflecting the significance of his contributions to cancer imaging and treatment. His work centers on developing novel machine learning and deep learning approaches for medical imaging analysis and precision oncology, with specific focus on image-guided and adaptive radiation therapy techniques. He has pioneered the discovery of imaging-based biomarkers that enable more accurate cancer detection, diagnosis, and treatment response prediction across multiple tumor types. His laboratory integrates radiology and histopathology imaging data with genomic information, creating comprehensive frameworks that reveal deeper insights into cancer biology and progression.
Beyond his research contributions, Dr. Li has received numerous prestigious awards including the Pathway to Independence Award (K99/R00) from NIH/NCI in 2012 and the iDEA-iTECH Award from Sanofi in 2023. He serves as a Postdoctoral Faculty Sponsor for numerous trainees at Stanford, guiding the next generation of researchers at the intersection of AI and medicine. Dr. Li maintains active leadership roles including membership on the Board of Associated Editors for AAPM (2019-2024) and service on the Scientific Review Panel for ASTRO. His recent work includes the development of a pathology foundation model for cancer diagnosis published in Nature 2024, which represents a major advancement in computational pathology with significant potential to transform cancer care through personalized treatment selection.
