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neurostimulators. Our team has deconstructed brain activity to discover the neural code responsible for the abnormality of walking in Parkinson’s disease and can predict debilitating freezing events that can cause
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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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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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most of our physiological responses to hormones, neurotransmitters and environmental stimulants. We employ an interdisciplinary approach to probe, model, and predict how signaling network dynamics
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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
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or crowding in predicting reading skills. In regards to EF, the field is progressing towards a consensus that EFs are important for reading and that EF deficits are common in dyslexia. But due to the limited
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brain imaging-derived circuit measures to identify biotypes of depression, understand how these biotypes relate to symptoms and behaviors, and predict personalized treatment outcomes. The postdoctoral