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amounts of clinical data are needed to train useful AI algorithms. However, patient data are person-sensitive, and only selected individuals can obtain access, which can be a significant roadblock for
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algorithms for representation learning, uncertainty quantification, and model interpretability over large, heterogeneous datasets such as sequenced microbial DNA fragments coupled with auxiliary environmental
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for human control of complex robotic systems with high levels of agency and minimal cognitive effort. Short description: This project will develop novel AI algorithms to decode human intention from
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