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Metasurfaces are artificial materials composed of sub-wavelength building blocks whose size, shape, periodicity and composition are tailored to engineer their optical response and achieve arbitrary control of their interactions with light. Their phase discontinuities or resonances are critically dependent upon the local dielectric or refractive index environment, thus making metasurfaces excellent candidates as refractive index […] |
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Optical metasurfaces enable strong light–matter interactions, making them ideal platforms for high–figure-of-merit (FOM) sensing. When fabricated from colloidal nanocrystal dispersions, these metasurfaces offer unique advantages in fabrication flexibility, reconfigurability, and cost-effectiveness. However, conventional fabrication approaches often rely on toxic material systems. In this thesis, we enhance the FOM of titanium dioxide (TiO₂)-based dielectric metasurfaces by […] |
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ESE Ph.D. Thesis Defense: “Statistical Limits and Efficient Algorithms for Learning-Enabled Control”
ESE Ph.D. Thesis Defense: “Statistical Limits and Efficient Algorithms for Learning-Enabled Control”
As the adoption of large-scale learning for control continues to grow, developing sample-efficient algorithms has become critical. Yet, even in simple settings, algorithms achieving optimal sample complexity for specific problem instances often remain unknown. Motivated by this limitation, we discuss recent progress toward understanding sample-efficient methods in learning-enabled control. We first examine the statistical limits […] |
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