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, architectures, and algorithms. Specifically, we investigate energy-efficient computer architecture for machine learning based on novel devices and computing principles, device-aware machine learning algorithms
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singular elliptic and parabolic PDEs, free boundary problems, optimal control of free boundary systems with distributed parameters. Current areas of interest include Potential Theory, Harmonic Analysis
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of concepts from quantum information to quantum materials; algorithmic aspects of quantum computing; topological aspects of magnetism; connections between condensed matter and high-energy physics
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