Click the projects below to see related publications, talks, and more info.
Interested in precision measurements of the Higgs boson, as well as searches for new Higgs-like particles to explain mysteries such as baryon asymmetry.
Leading the effort on several state-of-the-art generative models to accelerate LHC simulations. Developing as well validation and benchmarking schemes in order to bring them into CMS. Published at NeurIPS, PRD, and more.
Developed graph-based and Lorentz-equivariant models to better suit our high energy physics data. Our Lorentz-group autoencoder (LGAE) outperforms graph and convolutional networks on jet compression and anomaly detection tasks. Latest work published at EPJC.
Developed a library for convenient access to jet datasets, and other utilities, to increase accessibility and reproducibility in ML in particle physics. >35,000 downloads as of September 2023, used in several ML and particle physics projects.
Developed several approaches for finding rare particle collisions, including GNNs, Lorentz-equivariant networks, and multi-variate goodness-of-fit tests.
Interpreting results of machine learning models for reconstruction and jet classification using explainable AI techniques.
Created dynamic, sub-micron holographic optical tweezers and a Quantum Gas Microscope with sub-micron resolution in order to manipulate individual atoms (or qubits) for quantum computing and quantum information science experiments. This work won a William A. Lee Research award, and will be published soon.
Created an app for visualizing course requirements with a user-friendly UI. I was the Back-end and Algorithms Lead for a team of 10, and personally wrote the server, scraping and graphing algorithms for the app. We were one of 8 finalists out of 60 projects in the UCSD 2018 software engineering course.