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the United States as well as clinical imaging and testing data from Stanford. Project themes will include developing models using EHR data to predict outcomes in ophthalmology and glaucoma, as well as investigating
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NIH- and foundation-funded projects focused on treatment pathways, chronic pain phenotyping, pharmacoepidemiology, and biomarker-based prediction across autoimmune rheumatic diseases. This position
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, or machine learning experts to create predictive virtual 3D mammalian embryos for human health, especially congenital heart diseases. We welcome applicants with expertise in genomics, developmental biology
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of advancing healthcare through the development, evaluation, and application of innovative methods. Through her research, she aims to effectively monitor, measure, and predict equitable healthcare outcomes. Her
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labs at Stanford to tackle emerging clinical questions in oncology, utilizing various AI methods, predictive modeling approaches, and large language models. Specific areas of interest include but are not
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systems. Includes establishing medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user
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to explore brain-behavior relationships and predict cognitive outcomes Collaborate with a multidisciplinary team to develop interventions for Alzheimer’s-related dementia Publish findings in peer-reviewed
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transmission modeling, statistical modeling, spatial data analysis, and cost-effectiveness analysis. In parallel, we conduct research on vaccine-preventable infections, developing and evaluating predictive
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environmental stimulants. We employ an interdisciplinary approach to probe, model, and predict how signaling network dynamics translate extracellular cues sensed by GPCRs into specific phenotypic outputs
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predict disease progression? How can we better measure and mitigate the impact of biased training data for downstream clinical uses? Can we improve the factuality and reasoning of foundation models by