Projects
Click the projects below to see related publications, talks, and more info.
Higgs Searches
Interested in precision measurements of the Higgs boson, as well as searches for new Higgs-like particles to explain mysteries such as baryon asymmetry.
ML for Fast Simulations
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.
Equivariant Neural Networks
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.
JetNet
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.
Anomaly Detection
Developed several approaches for finding rare particle collisions, including GNNs, Lorentz-equivariant networks, and multi-variate goodness-of-fit tests.
Explainable AI
Interpreting results of machine learning models for reconstruction and jet classification using explainable AI techniques.
Optical Tweezers and a Quantum Gas Microscope
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.
GRAD: An interactive graph-based degree planning app
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.
Sequential Modeling for Soccer Predictions
Fun project to gain experience with RNNs and Attention. I achieved a 71% testing accuracy in predicting the outcome of European football matches.