Ph.D., Biomedical Engineering, Case Western Reserve University (2001)
\nPostdoctoral Fellowship, Medical Physics, Stanford University (2003)
\nM.S., Biophysics, Chinese Academy of Sciences (1997)
\nB.S., Applied Physics, Tsinghua University (1994)<\/p>\n
Therapeutic Radiological Physics (The American Board of Radiology) 2006<\/p>\n
2020 AAPM Grand Challenge “The Open Knowledge-Based Planning Challenge,” # 5 out of 100
\n2017 The Science Council Award, American Association of Physics in Medicine
\n2017 Best Protocol Development Award IBPRO (Integrated course in Biology and Physics of Radiation Oncology)
\n2014 The Chaney Award for Excellence in Physics Research, Department of Radiation Oncology, UNC Chapel Hill
\n2013 Editor\u2019s pick, Medical Physics Journal
\n2004 Top 10 most downloaded paper of Journal of Physiology
\n1997\u20132000 Whitaker Foundation Fellowship
\n1997 \u201cDi Au\u201d Graduates\u2019 thesis competition of Chinese Academy of Sciences, 2nd place
\n1995 Excellent Graduate Scholarship, Chinese Academy of Sciences<\/p>\n
Image segmentation and registration
\nMachine learning application in radiation oncology
\nIMRT\/VMAT optimization
\nImage-guided and adaptive radiotherapy<\/p>\n
\u201cImpact and Compensation of Intrafractional Motion for Tomotherapy Sterotactic Body Radiaotherapy Treatment (SBRT)\u201d, Accuray, 027214, 2017-2018. Principal Investigator<\/p>\n
\u201cAutomatic pelvic organ delineation in prostate cancer treatment\u201d, NIH 1R01CA206100, 2018-2023. Principal Investigator<\/p>\n
J. Lian, L.B. Marks, \u201cAdaptive Radiation Therapy for Lung Cancer,\u201d in Adaptive Radiation Therapy, page 293-311, edited by A.X. Li (CRC Press, 2011).<\/p>\n
Y. Gao, Y. Shi, S. Liao, J. Lian*, D. Shen, \u201cImage-Guided Radiotherapy with Machine Learning,\u201d in Machine Learning in Radiation Oncology: Theory and Applications, page 157-192, edited by Issam El Naqa, Ruijiang Li and Martin J. Murphy (* senior author, Springer International Publishing, 2015).<\/p>\n","protected":false},"excerpt":{"rendered":"