Stanford University
Showing 21-30 of 64 Results
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Syamantak Khan
Instructor, Radiation Oncology - Radiation Physics
Current Research and Scholarly InterestsIn-vitro bio-mimetic models of cancer and cancer metastasis
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Nataliya Kovalchuk
Clinical Associate Professor, Radiation Oncology - Radiation Physics
BioEducation:
2002 - B.S., Physics, Drohobych State University, Ukraine
2004 - M.S., Physics, Minnesota State University, Mankato, MN
2008 - Ph.D., Applied Physics, University of South Florida (H. Lee Moffitt Cancer Center and Research Institute), Tampa, FL
2010 - Medical Physics Residency, Mayo Clinic, Rochester, MN
Academic Appointments:
2010 - 2015 - Instructor, Harvard Medical School, Massachusetts General Hospital/Boston Medical Center, Department of Radiation Oncology, Boston, MA
2015 - 2019 - Clinical Assistant Professor, Stanford University, Department of Radiation Oncology, Stanford, CA
2019 - present - Clinical Associate Professor, Stanford University, Department of Radiation Oncology, Stanford, CA
2019 - present - Adjunct Associate Professor, MD Anderson Cancer Center/University of Texas, Houston, TX -
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.
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Lianli Liu
Clinical Assistant Professor, Radiation Oncology - Radiation Physics
Current Research and Scholarly InterestsAI-driven medical imaging for accelerated imaging speed and improved image quality, including:
Accelerated imaging for in-treatment patient monitoring and post-treatment patient follow up;
Functional imaging for treatment response evaluation and prediction.
Optimizing clinical quality assurance workflow through AI, including:
Radiation beam data modeling for efficient commissioning;
Model-based error detection for accurate dosimetry.