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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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sensing, energy-efficient computing systems, artificial intelligence (AI), and machine learning (ML), which has resulted in many collaborations with industry and other research institutes. Most of our
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-driven methodologies, including machine learning and AI-based experimental design, for accelerated formulation and evaluation of battery materials. This enables faster iteration cycles, improved
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