School of Medicine
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Yushen Qian, MD
Clinical Associate Professor, Radiation Oncology - Radiation Therapy
BioDr. Qian is a board-certified radiation oncologist and a Clinical Associate Professor in the Stanford University School of Medicine, Department of Radiation Oncology.
In his clinical practice, he sub-specializes in genitourinary (including prostate and bladder cancer) and Head and Neck malignancies, but also treats a broad spectrum of other disease subsites including lung/thoracic, gastrointestinal, brain, lymphoma, and breast tumors. For each patient, he develops a comprehensive, individualized, and compassionate care plan customized to individual needs. His goal is to deliver the most effective cancer treatment to help patients enjoy the best possible health and quality of life.
In addition to his clinical practice, Dr. Qian serves as the Medical Director of Radiation Oncology at Stanford South Bay Cancer Center. He also serves as the Radiation Oncology Network Director of Clinical Research and has spearheaded opening of multiple NRG Oncology clinical trials at Stanford South Bay Cancer Center.
Dr. Qian is also actively involved in the Stanford Radiation Oncology residency program. He created and oversees a monthly mentorship roundtable series to assist residents with multiple aspects of their clinical training and career progression.
Outside of work, Dr. Qian enjoys spending time with his family and exploring the great outdoors of Northern California. -
Xiaojie Qiu
Assistant Professor of Genetics and, by courtesy, of Computer Science
Current Research and Scholarly InterestsAt the Qiu Lab, our mission is to unravel and predict the intricacies of gene regulatory networks and cell-cell interactions pivotal in mammalian cell fate transitions over time and space, with a special emphasis on heart evolution, development, and disease. We are a dynamic and interdisciplinary team, harnessing the latest advancements in machine learning as well as single-cell and spatial genomics by integrating the predictive power of systems biology with the scalability of machine learning,
