School of Medicine
Showing 1-85 of 85 Results
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Jeff Choi
Masters Student in Biomedical Informatics, admitted Autumn 2020
Resident in Surgery - General SurgeryBioGeneral Surgery Resident (2017-) in professional development time. MSc student in Epidemiology & Clinical Research (2019-2020), and Biomedical Informatics (2020-). Co-President of SWAT (Surgeons Writing About Trauma); helping students get excited about clinical research is my passion.
My interest is encouraging cross-disciplinary collaborations to tackle challenging research questions in trauma surgery. To better lives of injured patients and their families, our research teams explore evidence synthesis (meta analysis), computer vision applications, decision analysis/cost-effectiveness analysis, epidemiological/clinical outcomes research, and prognostication tool development. If you are interested in collaborating, please reach out. -
Benjamin Huynh
Ph.D. Student in Biomedical Informatics, admitted Autumn 2017
BioI'm a PhD student in Biomedical Informatics, advised by Sanjay Basu. Previously, I obtained a BS in Statistics at the University of Chicago. My research interests include biostatistical methods and machine learning, with applications to public health and social justice.
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Timothy Keyes
MD Student, expected graduation Spring 2021
Ph.D. Student in Cancer Biology, admitted Winter 2018
MSTP Student
Masters Student in Biomedical Informatics, admitted Winter 2021BioTimothy is an MD/PhD student studying cancer biology and biomedical informatics at the Stanford University School of Medicine. He is a joint member of Kara Davis's laboratory in the Department of Pediatrics and Garry Nolan's Laboratory in the Department of Pathology.
As a biomedical data scientist, Timothy's research focuses on the application of machine learning to single-cell data analysis in the context of pediatric leukemia. Through the use of emerging, high-throughout single-cell technologies such as mass cytometry and sequence-based cytometry, Timothy's research is designed to build predictive models of patient outcomes - such as relapse or minimal residual disease (MRD) - at the point of diagnosis. To do so, he uses a variety of computational tools including generalized linear models, clustering, and deep learning. In addition, his work prioritizes constructing easy-to-use, highly-reproducible data analysis pipelines that can be shared as open-source tools for the scientific community.
Outside of science, Timothy has a longstanding interest in human rights and social justice work among members of the lesbian, gay, bisexual, transgender, and queer (LGBTQ+) community. He currently serves as the resident data scientist for the Medical Student Pride Alliance (MSPA), a 501(c)(3) non-profit organization that advocates for diversity, equity, and inclusion for LGBTQ+ medicals students in medical schools across the United States. As a data scientist at MSPA, Timothy analyzes and visualizes data to guide MSPA's strategic decision-making as well as for academic publication. He also advises and mentors other student members of MSPA performing data analysis in Python and R.
In recognition of his accomplishments, Timothy has received several institutional and national award for both research and advocacy. These include a National Research Service Award (NRSA) from the National Cancer Institute, a Junior Leadership Award from the Building the Next Generation of Academic Physicians (BNGAP) LGBT Workforce, Stanford Medicine’s Integrated Strategic Plan Star Award, and a Point Foundation Scholarship. -
Sophia Ying Wang, MD
Clinical Instructor, Ophthalmology
Masters Student in Biomedical Informatics, admitted Autumn 2019Current Research and Scholarly InterestsI use and integrate a wide variety of data sources in my research, spanning both structured and unstructured forms, including national survey datasets, health insurance claims data, patient generated online text, and electronic health records. I investigate outcomes of treatments for glaucoma and cataract, as well as other areas of ophthalmology, while developing and applying novel methods for automated extraction of ophthalmic data from free text.
