ESE Ph.D. Thesis Defense: “Novel van der Waals Chalcogenides for Sustainable Light Harvesting”
The global climate crisis demands a shift to renewable energy sources. Solar photovoltaics (PVs) are widely considered the most feasible renewable technology to meet global energy demands, and solar photo-electrocatalysis […]
ESE Guest Seminar: “On-Device Probabilistic AI: From Gaussian Transistors to Light-Driven Spike Encoding”
Emerging edge AI systems call for device-level approaches that are inherently low-power, secure, and capable of managing uncertainty. In this talk, I will share our recent exploratory efforts toward realizing […]
ESE Ph.D. Thesis Defense: “Energy-efficient Wavelength-Division-Multiplexing Systems for the Next Generation of Optical Transceivers”
The rapid growth of data traffic in data centers and AI applications demands faster, more energy-efficient communication solutions to scale up parallel computing capabilities. This dissertation explores integrated photonic-electronic systems […]
ESE Ph.D. Thesis Defense: “Towards General Microscopic Robots”
This defense presents my contributions towards general robotics at the microscopic scale. Namely, through the introduction of fully programmable, autonomous microscopic robots free to explore the microscopic world. The robots […]
ESE Ph.D. Thesis Defense: “Tunable Dielectric Nanocrystal Metasurfaces for Colorimetric Sensing”
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 […]
MSE Ph.D. Thesis: “Metasurfaces For Environmental Refractive Index Sensing: Design, Fabrication And Interrogation”
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 […]
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 […]