School of Medicine
Showing 1-50 of 120 Results
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Russ B. Altman
Kenneth Fong Professor and Professor of Bioengineering, of Genetics, of Medicine (General Medical Discipline), of Biomedical Data Science and, by courtesy, of Computer Science
On Partial Leave from 01/01/2021 To 06/30/2021Current Research and Scholarly InterestsI refer you to my web page for detailed list of interests, projects and publications. In addition to pressing the link here, you can search "Russ Altman" on http://www.google.com/
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Alison Callahan
Research Engineer, Med/BMIR
BioAlison Callahan is a research scientist in the Center for Biomedical Informatics and a member of the Shah Lab. 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.
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. -
Jonathan H. Chen, MD, PhD
Assistant Professor of Medicine (Biomedical Informatics) at the Stanford University Medical Center
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 Student Employee, Medicine - Med/PCORBioI am a PhD candidate in the Department of Biomedical Data Science at Stanford University, advised by Euan Ashley and Trevor Hastie. My research focuses on promoting health equity by addressing disparities in chronic illness and mass incarceration. My work centers around integrating data from disparate sources using a variety of quantitative approaches such as machine learning, simulations, and inference.
I am interested in occupational, socioeconomic, and racial inequities and their relation to public health. My most recent projects include:
1. Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures
2. Frequency of Routine Testing for Covid-19 in High-risk Healthcare Environments to Reduce Outbreaks
3. Testing strategies for airline travel during the COVID-19 pandemic
4. Studying decarceration, ongoing risks, and risk factors from Covid-19 in the California State Prison System
5. Quantifying disparities in testing and vaccine acceptance in California prisons
I am grateful for the support of the Stanford Graduate Fellowship and the National Science Foundation Graduate Fellowship. Before coming to Stanford, I obtained my Bachelor of Science in Applied Mathematics at the University of California Los Angeles, where I was fortunate to conduct computational genomics research under the advisement of Xinshu (Grace) Xiao. As an undergraduate, I was also honored to conduct research with Rachel Martin, Carter Butts, and Pardis Sabeti as part of NSF REU and Harvard Systems Biology Summer Research Programs. I have also worked on machine learning/data science teams at Adobe Systems and Quora.
If you’re interested in my work or share interests, don’t hesitate to reach out. You can contact me at etchin at stanford.edu. -
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. -
Jean Coquet
Biomedical Informatician, Med/BMIR
BioI received my PhD from University of Rennes 1, where I was working in the Dyliss team (DYnamics, Logics and Inference for biological Systems and Sequences), at the INRIA institute (Rennes, France). My research area have focused on making sense of unconventional and complex wide data biological sets, such as signaling pathways, gene interactions, or more recently, Electronical Health Records (EHRs). In Boussard Lab, my research is to establish different novel strategies for the evaluation the quality healthcare delivery, involving machine learning and Natural Language Processing (NLP) methods.
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Deendayal Dinakarpandian
Clinical Associate Professor, Medicine - Biomedical Informatics Research
Current Research and Scholarly InterestsMethod development and insightful informatics based on my training as a physician, biochemist and computer scientist: Methods for representing, capturing and integrating emerging or expert biomedical knowledge to improve computational predictions of biological and clinical relevance. Methods for evaluating predictions based on machine learning. Interventional and causal predictions. Informatics research on problems in oncology, radiology and allergy/immunology.
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Norman Downing
Clinical Assistant Professor, Medicine - Biomedical Informatics Research
BioI am a faculty member in Biomedical Informatics Research at Stanford and board-certified internal medicine and clinical informatics. I split my time between clinical practice, hospital medical informatics and applications of artificial intelligence in healthcare. I work with the Clinical Excellence Research Center – a research group dedicated to reducing the cost of high-quality care – directing the Partnership in AI collaboration with the Stanford Artificial Intelligence Lab. Recognizing that the complexity of medicine has grown beyond the abilities of even the most expert clinician, we focus applications of computer vision to address some of the greatest challenges in healthcare: perfecting intended care for frail patients in settings ranging from the intensive care unit to the home. I have published work in the New England Journal of Medicine, Health Affairs, Annals of Internal Medicine, and the Journal of the American Medical Informatics Association. My interests include a design-based approach to understand how technology has impacted the work of clinicians and implications for new care models, workflow, and technology integration.
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Matthew A. Eisenberg
Clinical Assistant Professor (Affiliated) [Shc], Med/BMIR
Staff, Med/BMIR - Division ChiefBioDr. Matthew A. Eisenberg joined Stanford Health Care in early 2013 and is the Medical Informatics Director for Analytics & Innovation with a focus on interoperability and health information exchange, regulatory reporting, health care analytics, patient reported outcomes and other uses of technology to meet our strategic initiatives.
Dr. Eisenberg is board certified in Pediatrics and Clinical Informatics. He is a Clinical Assistant Professor (Affiliated) in the Stanford Center for Biomedical Informatics Research at the Stanford University School of Medicine and he serves as the Stanford Health Care site director for the Stanford Clinical Informatics Fellowship Program. He previously held the position of Clinical Assistant Professor in Pediatrics at the University of Washington School of Medicine. He is a current member of the eHealth Exchange Coordinating Committee, a Sequoia Project Board member and serves as the current chair of the Epic Care Everywhere Network Governing Council. He is a member of the Carequality Advisory Council (past co-chair) and a member of IHE USA Implementation Committee. He is a Fellow of the American Academy of Pediatrics and a member of the American Medical Informatics Association and their Clinical Informatics Community. -
Olivier Gevaert
Assistant Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsMy lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. We primarily use methods based on regularized linear regression to accomplish this. We primarily focus on applications in oncology and neuroscience.
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John Graybeal
Rsch Technical Mgr 2, Med/BMIR
Current Role at StanfordJohn is a Technical Program Manager at Stanford University's School of Medicine. He leads the Center for Enhanced Data Annotation and Retrieval (CEDAR), and the NCBO BioPortal Repository, .
John's work encompasses whatever is needed: project management, product management, systems architecture, dev ops, and administration, to name a few fun roles. -
Summer Han
Assistant Professor (Research) of Neurosurgery and of Medicine (Biomedical Informatics)
Current Research and Scholarly InterestsMy current research focuses on understanding the genetic and environmental etiology of complex disease and developing and evaluating efficient screening strategies based on etiological understanding. The areas of my research interests include statistical genetics, molecular epidemiology, cancer screening, health policy modeling, and risk prediction modeling. I have developed various statistical methods to analyze high-dimensional data to identify genetic and environmental risk factors and their interactions for complex disease.
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Josef Hardi
Software Dvlpr 2, Med/BMIR
BioI'm a software engineer with a keen interest in data science. I have over 10 years’ experience in software development and 5 years in the data processing. Currently, I work as a backend developer for the Stanford Center of Biomedical Informatics Research; tackling issues in data and metadata management and interoperability. I also actively engage in the work of converting health and claim records to the OMOP common data model as part of my collaboration with the Stanford Population Health Sciences. I have experience with Java, Python, R, RDF, OWL, OBDA, Schema.org and Elasticsearch.
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Zihuai He
Assistant Professor (Research) of Neurology and of Medicine (BMIR)
BioDr. He received his PhD from the University of Michigan in 2016. Following a postdoctoral training in biostatistics at Columbia University, he joined Stanford University as an assistant professor of neurology and of medicine in 2018. His research is concentrated in the area of statistical genetics and integrative analysis of omics data, with the aim of developing novel statistical and computational methodologies for the identification and interpretation of complex biological pathways involved in human diseases, particularly neurological disorders. His methodology interest includes high-dimensional data analysis, correlated (longitudinal, familial) data analysis and machine learning algorithms.
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Tina Hernandez-Boussard
Associate Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsMy background and expertise is in the field of computational biology, with concentration in health services research. A key focus of my research is to apply novel methods and tools to large clinical datasets for hypothesis generation, comparative effectiveness research, and the evaluation of quality healthcare delivery. My research involves managing and manipulating big data, which range from administrative claims data to electronic health records, and applying novel biostatistical techniques to innovatively assess clinical and policy related research questions at the population level. This research enables us to create formal, statistically rigid, evaluations of healthcare data using unique combinations of large datasets.
