School of Medicine
Showing 51-100 of 142 Results
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Summer Han
Assistant Professor (Research) of Neurosurgery, of Medicine (Biomedical Informatics) and, by courtesy, of Epidemiology and Population Health
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.
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Timothy Keyes
MD Student, expected graduation Spring 2023
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. -
Teri Klein
Professor (Research) of Biomedical Data Science, of Medicine (BMIR) and, by courtesy, of Genetics
Current Research and Scholarly InterestsCo-founder, Pacific Symposium on Biocomputing
NIEHS, Site Visit Reviewer
NIH, Study Section Reviewer -
Curtis Langlotz
Professor of Radiology (Thoracic Imaging) and of Biomedical Informatics Research
Current Research and Scholarly InterestsI am interested in the use of deep neural networks and other machine learning technologies to help radiologists detect disease and eliminate diagnostic errors. My laboratory is developing deep neural networks that detect and classify disease on medical images. We also develop natural language processing methods that use the narrative radiology report to create large annotated image training sets for supervised machine learning experiments.
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Maya Mathur
Assistant Professor (Research) of Pediatrics, of Medicine (Biomedical Informatics) and, by courtesy, of Epidemiology and Population Health
BioMy website has moved: www.mayamathur.com.
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Mark Musen
Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsIt's important to ensuring that experimental data—and descriptions of the methods used to generate and analyze the data—are available online. Our laboratory studies methods for creating more comprehensive metadata descriptions both of data and of experiments that can be processed both by other scientists and by computers. We are also working to clean up legacy data and metadata to facilitate open science broadly. Other work focuses on management of knowledge using knowledge graphs.
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Behzad Naderalvojoud
Postdoctoral Scholar, Biomedical Informatics
BioBehzad Naderalvojoud is a Postdoctoral Research Fellow at Stanford Center for Biomedical Informatics Research. He received his Ph.D. degree in Computer Science at Hacettepe University, Turkey, in 2020. He is immersed in the fields of machine learning, deep learning, natural language understanding, and Big data analytics and works on health knowledge discovery platforms that transfer Big Health data from volume-based to value-based by generating relational knowledge leading to innovative treatments, predictive therapeutic outcomes, and early diagnosis. He was the leader of many industrial AI projects in the fields of healthcare intelligence and information management in the Eureka cluster programs.
Dr. Naderalvojoud has published several papers in the field of natural language understanding by working on word sense disambiguation, sentiment analysis, neural word embeddings, and deep learning models through national and international projects.
He is currently working on the funded NLM grant project "Advancing Knowledge Discovery for Postoperative Pain Management" under the supervision of Dr. Tina Hernandez-Boussard. He develops descriptive, predictive, and analytical tools using OMOP CDM for postoperative pain research to facilitate timely generation of evidence across multiple populations and settings. -
Sandy Napel
Professor of Radiology (Integrative Biomedical Imaging Informatics) and, by courtesy, of Medicine (Medical Informatics) and of Electrical Engineering
Current Research and Scholarly InterestsMy research seeks to advance the clinical and basic sciences in radiology, while improving our understanding of biology and the manifestations of disease, by pioneering methods in the information sciences that integrate imaging, clinical and molecular data. A current focus is on content-based radiological image retrieval and integration of imaging features with clinical and molecular data for diagnostic, prognostic, and therapy planning decision support.
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Minh Nguyen
Ph.D. Student in Biomedical Informatics, admitted Autumn 2018
Ph.D. Minor, Management Science and EngineeringBiohttps://datascience.stanford.edu/people/minh-nguyen
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Natalie Pageler
Clinical Professor, Pediatrics - Critical Care
Clinical Professor, Medicine - Biomedical Informatics ResearchCurrent Research and Scholarly InterestsIn my administrative role, I oversee the development and maintenance of clinical decision support tools within the electronic medical record. These clinical decision support tools are designed to enhance patient safety, efficiency, and quality of care. My research focuses on rigorously evaluating--1) how these tools affect clinician knowledge, attitudes, and behaviors; and 2) how these tools affect clinical outcomes and efficiency of health care delivery.
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Jonathan P. Palma
Clinical Professor, Medicine - Biomedical Informatics Research
Current Research and Scholarly InterestsInterventional informatics to achieve examples of a learning healthcare system; optimization of commercial EMRs to support complex clinical workflows in newborn intensive care; clinical decision support; real-time clinical dashboards; electronic sign-out tools; IT-supported patient/family communication.
