San Francisco Bay Area
From gigantic luminous pulsars to the subatomic interactions of quarks in atoms, physics is the study of the universe, offering intricate real-world implications and awe-inspiring theoretical beauties. Math is the elegant language underlying all this aforementioned physics. These two subjects spark a blazing flame of curiosity within me, and therein lies my passion to study them! Contact: [email protected]
Quantum networking research focused on bridging neutral atom and superconducting qubit platforms. - Built and optimized an atomic system featuring a magneto-optical trap (MOT) and optical lattice to trap, cool, and transport Rb-87 atoms for quantum networking applications. - Designed mechanical components in CAD and machined custom parts; built and aligned optical systems for laser-based atom trapping and cooling. - Developing microwave electronics for measurement and control operations on a transmon qubit. - Engineering high-frequency interconnects for superconducting circuits for entanglement distribution.
- Tutored students in electromagnetism, optics, and modern physics (Physics 8B). - Prepared relevant study materials (Ex: mini-lessons or practice problems). - Attended weekly seminars aimed towards improving my teaching abilities.
- In association with a PhD student, through the PDRP I've researched and studied the field of neutral atom quantum computing. - Delved into the theory behind relevant topics such as laser cooling and trapping, optical tweezers, laser-addressed gate operations, acousto-optical deflectors, and Rydberg-blockades. - Beginning to explore quantum error corrections from a neutral atom qubit perspective.
At Berkeley Lab’s ATLAS collaboration I develop and apply machine learning methods to advance Higgs boson physics. - Researching the interpretability of foundational models in high energy particle physics. - Creating novel methods for decay chain reconstruction in collider analyses using Graph Neural Networks and attention mechanisms. - Conducted a proof-of-concept di-Higgs analysis aimed at making measurements of the Higgs self-coupling and the shape of the Higgs potential by studying Higgs pair production in the HH --> yy + X final state. - Building training and inference pipelines for a large-scale di-Higgs analysis.
Grew the presence of QCB on-campus while simultaneously increasing member count and member retention rate.
- Gave weekly lectures on topics ranging from the theory behind quantum computation to the theory behind quantum error correction and error mitigation. - Organized weekly club meetings and events as part of a leadership team. - Developed a computational project in Python utilizing quantum toolkits such as Qiskit, Cirq, and Mitiq to study quantum error correction and error mitigation.
- Through the 2023-2024 school year, I learned a lot about the theory and experimental implementations of Quantum Error Corrections. This includes delving into topics such as topological quantum error correcting codes, surface codes, and erasure conversion on metastable qubits in trapped ion platforms. - Participated in the QRISE 2024 research exchange where I explored novel methods of stacked quantum error mitigation techniques using the Mitiq Python toolkit in tandem with Cirq and Qiskit. - Took part in reading groups where we would go through research papers and articles and present learnings to the club as a whole.
- Utilized simulations and data analysis tools to conduct sensitivity and error quantification studies of proprietary drone-mounted muon hodoscope hardware. - Simulations optimization.
- Setup Geant4 Monte Carlo simulation to simulate the operation of proprietary muon hodoscopes for the purpose of muon tomography and muon flux simulations. Includes lots of geometric modeling, simulation efficiency optimizations, and C++ simulation code. - Implemented Cosmic Ray Shower Library (CRY) for cosmic ray muon generation and several unique experimental setups with varying geometries for validation of hodoscope design and hardware due diligence for investors. - Worked towards generating simulated muon flux data to eventually be fed into Bayesian Inversion Models to generate 3D voxel distributions representing overburden structure though MCMC and triangulation methods.
- Studied the field of jet flavor tagging, more specifically B-jet tagging, in association with the PDRP and a physics PhD student. Picked up elementary knowledge on particle physics and machine learning in a physics context along the way. - Performed data analysis, visualization, and statistical machine learning to classify between B-jets and other jet variants in a dataset of Monte Carlo simulated proton-proton collisions from the CMS experiment at the LHC. - Achieved upwards of 90% accuracy utilizing machine learning classification methods such as decision trees, random forests, KNN, and neural networks. - Presented findings and educational materials at an end of semester poster session with many peers, professors, post docs, and faculty present.