Stanford University


Showing 91-100 of 185 Results

  • Wenting Chen

    Wenting Chen

    Postdoctoral Scholar, Radiation Physics

    BioI am currently a Postdoc Fellow in the Department of Radiation Oncology of Stanford University, advised by Prof. Lei Xing. Before joining Stanford, I obtained my Ph.D degree in the Department of Electrical Engineering, City University of Hong Kong, supervised by Prof. Yixuan YUAN, Prof. W.S Tommy Chow, and Prof. L.H. Leanne Chan. I visited Massachusetts General Hospital and Harvard Medical School, supervised by Prof. Xiang Li and Prof. Quanzheng Li. Before that, I received the B. Eng and M. Eng degree from College of Computer Science and Software Engineering in Shenzhen University of China in 2017 and 2020, supervised by Prof. Linlin Shen. From Dec. 2019 to Nov. 2020, I had interned in Tencent Jarvis Lab, supervised by Dr. Shuang Yu and Prof. Yefeng Zheng.

    My research interests lie in vision-language model, multi-modal large language model, generative AI, computer vision and their applications on medical AI, with a focus on report generation, medical image synthesis, endoscopy super-resolution, retinal image segmentation, multi-modality diagnosis, etc.

  • Xiangjun Chen

    Xiangjun Chen

    Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine

    BioDr. Xiangjun Chen is a Postdoctoral Scholar in the Department of Anesthesiology, Perioperative, and Pain Medicine at Stanford University. He earned his Ph.D. in Materials Science from UC San Diego, where he also completed a postdoctoral training prior to joining Stanford. His work focuses on engineering soft wearable systems for healthcare monitoring, AI-driven human-machine interfaces, and advanced actuators and sensors for soft robotics.

  • Yiyun Chen

    Yiyun Chen

    Postdoctoral Scholar, Stanford Cancer Institute

    BioYiyun Chen is a computational cancer immunologist whose interdisciplinary training spans structural biology, computational genomics, and cancer immunotherapy. During her doctoral training at HKUST, she developed multi-omic frameworks to decode the molecular landscape of brain tumors, gastric cancer, and B cell lymphoma — including the discovery of a tumor-associated monocyte population in the glioma microenvironment that drives mesenchymal transformation through the FOSL2-EREG/AREG-EGFR signaling axis.

    As a postdoctoral fellow in the Crystal Mackall Laboratory at Stanford Cancer Institute, she extended this focus to the co-evolution of glioma and the immune system during CAR T cell therapy, uncovering multiple mechanisms of acquired resistance: anti-CAR humoral and cellular immunity, proinflammatory-to-immunosuppressive phenotypic shifts in macrophages, and tumor antigen escape. Her future research program will build an AI-powered platform that integrates longitudinal single-cell and spatial transcriptomics to model tumor-immune co-evolution in silico — constructing patient-level digital twins that simulate treatment trajectories, predict resistance, and identify real-time monitoring biomarkers.

  • Yunwei Chen

    Yunwei Chen

    Postdoctoral Scholar, Economics

    BioYunwei Chen is a Postdoctoral Scholar at Stanford University, affiliated with the Stanford Center on China's Economy and Institutions at the Freeman Spogli Institute for International Studies. She is also a Postdoctoral Fellow with the Stanford Impact Labs and a Global Health Postdoctoral Affiliate with the Stanford Center for Innovation in Global Health.

    Her academic training is in global health economics. Prior to joining Stanford, she earned a PhD in Health Policy and Management (Economics Track) from the Gillings School of Global Public Health at the University of North Carolina at Chapel Hill in 2024.

    Her research explores innovative solutions for effective delivery of public health interventions in resource-limited settings through rigorous experimental and quasi-experimental designs. Her current research agenda is centered on integrating digital health technologies to develop comprehensive and tailored interventions for children and mothers living in resource-limited settings during crucial developmental stages, aiming for both effectiveness and scalability.