I'm currently a research assistant to Prof. Karan Mehta in the Photonics and Quantum Electronics Group at Cornell. My research centers on investigating high-coherence control methods for trapped-ion quantum information processing devices. There are experimental and computational aspects to my work.

My experimental work currently involves setting up a fiber noise cancelation system for the 729nm laser system used for ion qubit addressing. Drawing upon on the method outlined in Ma et. al. (1994), I led the layout and implementation of the beam path, and created the bill of materials for the overall system. Working with PhD candidate Hamim Mahmud Rivy, I gained a strong background in using experimental optics equipment, laser beam alignment techniques, and foundational photonics principles.

My computational research involves simulating systems for high-coherence ion qubit manipulation. During the summer of 2023, I developed a novel algorithm to optimize permanent magnet shapes for high-uniformity magnetic field generation. These high-uniformity magnetic fields would be integral for ensuring stable Zeeman level splitting in ions at different regions of the ion trap, thus mitigating ion qubit dephasing. Implemented in Python, my algorithm was able to optimize magnet geometries to result in a 1000x improvement in field uniformity over existing designs, suggesting that there could be significant benefits in using these optimized geometries to improve ion qubit coherence.

I am also working on numerically simulating surface electrode trap potentials for ion confinement. I first characterized the ion trap electrodes in Python, determining the spatial electric potential distribution due to a unit voltage applied to each electrode. This is based on a method based on Allcock et. al. (2011). Following that, I generated different basis voltage sets for different trapping potential configurations, i.e. axial, tilt, y-compensation. I am currently working to optimize the trap depth of the total trapping potential seen by the ion.

Developed machine learning-integrated embedded systems for human activity recognition to improve safety of at-risk persons. Collected and characterized movement data for a variety of activities (e.g. sitting, walking, climbing stairs) using embedded networked sensors. Designed and trained self-designed Tensorflow LSTM deep neural networks and support vector machines (SVMs) on processed data to categorize activities using real-time sensor inputs with accuracy >97%.

Presented project at finals of the Singapore Science and Engineering Fair 2018.