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. We develop machine learning methods tailored for high-dimensional, multimodal biological data, with applications ranging from single-cell genomics to real-world clinical datasets. We are active
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, calibration, and the development of analysis tools and software. Our key focus areas are the physics of jets, top quarks, and EWSB, including the development of novel machine-learning methods for high-energy
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