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Daniel Reker is an Assistant Professor in the Department of Biomedical Engineering at Duke University. His research focuses on the integration of active machine learning methodologies with biomedical data science, aimed at enhancing the analysis and design of biochemical experiments to formulate personalized therapeutic opportunities. The Reker lab is dedicated to developing automated experimentation frameworks that utilize active machine learning to produce comprehensive, knowledge-rich datasets. A significant aspect of Reker's work is aimed at understanding the efficacy, biodistribution, metabolism, toxicity, and side effects of drug properties. His research addresses the predictive modeling needs in drug discovery, striving to refine processes and analyze clinical data rapidly to inform precision medicine strategies. With a Ph.D. from the Swiss Federal Institute of Technology-ETH Zurich, he employs innovative methodologies in machine learning to bridge gaps in the current understanding of drug interactions and development processes.
Duke University • Durham, NC
Research and teaching in Biomedical Engineering, focusing on machine learning applications in drug discovery and development.
Department of Biomedical Engineering (MS program)