School of Medicine
Showing 1-10 of 11 Results
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Alison Callahan
Instructor, Medicine - Biomedical Informatics Research
BioAlison Callahan is an Instructor and Clinical Data Scientist in the Center for Biomedical Informatics. In collaboration with Nigam Shah's group, her work involves research and development of informatics methods for the analysis of biomedical and clinical data to derive insights and inform medical decision making. Her current research focuses on using informatics to expand and improve the data available about pregnancy and birth, including developing an obstetric database from Stanford Health Care EHRs.
Alison completed her PhD in the Department of Biology at Carleton University in Ottawa, Canada. Her doctoral research focused on developing HyQue, a framework for representing and evaluating scientific hypotheses, and applying this framework to discover genes related to aging. She was also a developer for Bio2RDF, an open-source project to build and provide the largest network of Linked Data for the life sciences. Her postdoctoral work at Stanford applied methodologies developed during her PhD to study spinal cord injury in model organisms and humans in a collaboration with scientists at the University of Miami. -
Angelo Capodici
Affiliate, Med/BMIR
Visiting Scholar, Med/BMIRBioI am a Medical Doctor, and resident in Public Health at University of Bologna - Italy.
Currently a Visiting Scholar at Stanford, I am particularly interested in health services research and data use to optimize health care delivery for patients.
I am enthusiastic to collaborate on new challenges; feel free to contact me if you think I could help you with your project. -
Jonathan H. Chen, MD, PhD
Assistant Professor of Medicine (Biomedical Informatics)
Current Research and Scholarly InterestsInformatics solutions ares the only credible approach to systematically address challenges of escalating complexity in healthcare. Tapping into real-world clinical data streams like electronic medical records will reveal the community's latent knowledge in a reproducible form. Delivering this back as clinical decision support will uniquely close the loop on a continuously learning health system.
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Elizabeth (Liz) Chin
Ph.D. Student in Biomedical Informatics, admitted Autumn 2017
Stanford Stdnt Employee-Summer, Medicine - Med/PCORBioI am a PhD candidate in the Department of Biomedical Data Science at Stanford University, advised by Euan Ashley and Trevor Hastie. The overarching goal of my research is to create targeted interventions to aid medically vulnerable and marginalized populations to prevent poor health outcomes and the social determinants of these outcomes. My work centers around integrating data from disparate sources using a variety of quantitative approaches such as machine learning, simulations, and inference.
My research was generously funded by the National Science Foundation Graduate Fellowship and Stanford Graduate Fellowship. Previously, I obtained my BS in Applied Mathematics at UCLA, where I worked with Xinshu (Grace) Xiao. I have also worked under the guidance of Rachel Martin, Carter Butts, and Pardis Sabeti, and as a machine learning scientist at Adobe Systems and Quora.
If you’re interested in my work or share interests, don’t hesitate to reach out. I am also on the 2021-2022 academic job market. You can contact me at etchin at stanford.edu or follow me on Twitter.
You can find my most recent information and CV on my website, etchin.github.io.
