• Published highly-cited machine learning papers in leading journals and conferences by developing new neural network architectures to solve complex problems• Resolved core modeling challenge in photonic inverse design by developing a novel deep neural network architecture in Python, deployed on high performance computing systems• Achieved three-fold improvement in error for thin film design by integrating a deep mixture density network with a novel post-processing optimization algorithm• Designed a selective thermal emitter for photovoltaic cells that outperformed existing benchmarks by using transfer learning, meta learning, and recurrent neural network models• Achieved ten-fold reduction in data requirements to train computer vision model for cell recognition by integrating with optical techniques • Integrated natural language processing techniques to improve modeling results in nanophotonics by designing experiments and setting research agendas• Wrote simulation code in Python to generate large datasets, performed data cleaning and engineering operations to improve model training• Conducted outlook on integrating materials science machine learning with advancements in generative AI using LLM architectures and foundation models

• Discovered insights into electron dynamics in 2D materials by performing rigorous data analysis and statistical tests• Wrote code in Python and performed experimental validations for 3D reconstruction of images from microscopy data

• Designed an improved microwave resonator for improved filtering of frequencies for quantum physics data• Performed simulations and data analysis on qubits in dilution refrigerator for quantum computing applications