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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured biological data are increasingly common in modern
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such as mechanistic, chemometric, deep learning, and physics-aware models. Improve robustness and reliability of the developed methods for deploying AI models in real environments utilizing augmentation
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motor concepts and provide technical leadership for research initiatives focused on high power density, efficiency, reliability, manufacturability, thermal performance, and reduced reliance on critical