School of Medicine
Showing 21-40 of 186 Results
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Zheng Yan
Masters Student in Computer Science, admitted Autumn 2017
Current Role at Stanford I'm currently working part time for Dr. Kari Nadeau in the Pulmonary and Critical Care department as a bioinformatician - I get lots of awesome immunological data to work with R. My most recent project involves a study on twins, with the goal of correlating environmental factors with variations in cell frequency, serum proteins, and cytokine activation. I am mentored by Dr. Sandra Andorf.
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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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Fan Yang
Postdoctoral Research Fellow, Pathology
Bio Fan Yang has a broad background in Computational Biology, Genomics, Oncology, Immunology, and their intersections. She did her Ph.D. at the University of Toronto and participated in several inspiring and cutting-edge projects focusing on assessing the functionality and immunogenicity of human genomic variants both experimentally and computationally. She joined the Boyd lab at Stanford for her postdoctoral work to study the B cell and T cell repertoires in human infectious diseases and vaccine responses.
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Grant Yang
Affiliate, Rad/Radiological Sciences Laboratory
Bio Grant is developing novel diffusion MRI encoding and modeling schemes to improve the specificity of diffusion MRI (dMRI) measurements to features of tissue microstructure.
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Mi Yang
Postdoctoral Research Fellow, Oncology
Bio I am a bioinformatics scientist working in cancer immunotherapy. Originally trained as a pharmacist, I then did a PhD in machine learning applied to cancer drug screenings. My main areas of interest are: cancer immunotherapy and drug discovery. I am currently developing a reverse translational framework for drug discovery in DLBCL and building an interaction network to capture tumor microenvironment signaling and cell cell interactions. My overarching goal is to develop new immunotherapies for cancer and make the drug development process more efficient, rational and data driven.
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Phillip C. Yang, MD
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.
