School of Medicine
Showing 1-10 of 63 Results
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Fan Yang
Associate Professor of Orthopaedic Surgery and of Bioengineering
Current Research and Scholarly Interests Our research seeks to understand how microenvironmental cues regulate stem cell fate, and to develop novel biomaterials and stem cell-based therapeutics for tissue engineering and regenerative medicine. Our work spans from fundamental science, technology development, to translational research.We are particularly interested in developing better therapies for treating musculoskeletal diseases, cardiovascular diseases and cancer.
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Phillip C. Yang, MD
Associate Professor of Medicine (Cardiovascular Medicine) at the Stanford University Medical Center
Current Research and Scholarly Interests Dr. Yang is a physician-scientist whose research interest focuses on clinical translation of the fundamental molecular and cellular processes of myocardial restoration. His research employs novel in vivo multi-modality molecular and cellular imaging technology to translate the basic innovation in cardiovascular pluripotent stem cell biologics. Dr. Yang is currently a PI on the NIH/NHLBI funded CCTRN UM1 grant, which is designed to conduct multi-center clinical trial on novel biological therapy.
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Samuel Yang, MD, FACEP
Associate Professor of Emergency Medicine at the Stanford University Medical Center
Current Research and Scholarly Interests Dr. Yang's research is focused on bridging the translational gap at the interface of molecular biology, genome science, engineering, and acute care medicine. The investigative interest of the Yang lab falls within the general theme of developing integrative systems-level approaches for precision diagnostics, as well as data driven knowledge discoveries, to improve the health outcome and our understanding of complex critical illnesses. Using sepsis as the disease model with complex host-pathogen dynamics, the goals of the Yang lab are divided into 2 areas:
1) Developing high-content, near-patient, diagnostic system for rapid broad pathogen detection and characterization.
2) Integrating multi-omics molecular and phenotypic data layers with novel computational approaches into AI-assisted diagnostics and predictive analytics for sepsis.
