I am a researcher working at the intersection of physics, machine learning, and quantum computing.
At JPMorgan Chase's Center for Global Technology Applied Research, I develop algorithmic solutions
that bridge fundamental scientific principles with practical applications in finance, optimization,
and computational modeling.
Three Research Pillars
Physics-Informed Algorithms: Machine learning frameworks that integrate physical
principles for sensing, vision, and computational modeling. Led ML development for HADAR thermal
sensing technology (Nature 2023).
Quantum Approximate Optimization: Practical quantum algorithms for combinatorial
problems, with focus on parameter optimization, trainability, and applications to computational finance
(Quantum Journal 2024, Nature Communications 2024).
Quantum Machine Learning: Investigation of quantum approaches to learning, including
privacy characterization and variational methods for physical systems (npj Quantum Information 2025).
My work combines rigorous theoretical foundations with practical algorithm development. Prior research
includes quantum machine learning for electronic structure calculations, variational quantum algorithms
for quantum chemistry, and machine learning applications in photonics. I aim to develop methods that are
both scientifically rigorous and applicable to real-world problems in industry and scientific computing.
