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physics-integrated machine learning models—to predict, analyze, engineer, and understand microbial community dynamics. Applications span precision medicine and built environment microbiomes, with a strong
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, operations and clinical teams to identify opportunities for revenue optimization. Oversee the design and maintenance of dashboards, KPIs and predictive models for revenue cycle management. Oversee the volume
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predictive models to quantify shrubification risk; and (iii) investigate the implications of shrubification for water markets and policy. Teaching & Engagement: Contribute to climate fluency and sustainability
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, numerical methods, and Earth system modeling to develop and evaluate a coupled xylem–phloem transport framework that translates multiscale physics into next-generation vegetation model schemes. Key
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centers, including the Department of Population Health Sciences (DPHS). The Duke PopHealth DataShare™ (https://populationhealth.duke.edu/research/pophealth-datashare ) is a shared resource managed in DPHS
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, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control the dynamics of microbial communities in time and space. Ongoing projects
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that enhance population health programs and clinical enterprise operations. Work with quantitative sciences and clinical faculty to evaluate predictive models and innovation pilots and help disseminate results
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oversee the work of junior staff and train or mentor others in clinical research tasks. This study aims to enhance, deploy, and rigorously validate the Duke Predictive Model of Adolescent Mental Health