I am a physics PhD student at the Massachusetts Institute of Technology (MIT). My background is in particle physics, mathematics, data science, and applied sciences in engineering.
Quantum Field Theory
Jets and QCD
Beyond the Standard Model
I am primarily interested in the intersection between theoretical particle physics and modern machine learning methods. How can we use machine learning to understand particle physics, and how can we use particle physics to understand machine learning?
We show that the Earth Mover's Distance is the natural structure for comparing collider events and constructing IRC-safe observables. We also present SHAPER, a framework for defining and computing shape-based observables, and show how these new observables may be useful for phenomenological studies.
We demonstrate how some recent proposals for both simulation-based and data-based calibrations inherit properties of the sample used for training, which can result in biases for downstream analyses, and argue that our recently proposed Gaussian Ansatz approach can avoid some of the pitfalls of prior dependence.
We present a machine learning framework for performing maximum likelihood inference with Guassian uncertainty estimation, which also quantifies the mutual informaiton between the unobservable and measured quantities.
We search for evidence of Vector-Like Quark (VLQ) production in the CMS Detector of the Large Hadron Collider's entire second run, and set (as of publication) the highest mass limits on VLQ hadronic decays
Below is a list of all public presentations I have given, organized by topic (in no particular order). Note that while several talks share the same category and even the same title, the talks themselves can be very different, especially if the format (invited seminar versus talk, for example) is different! Unfortunately, many slides originally had GIFs and animations, which will not render here as PDFs.
Items marked with a red star (★) are my recommended versions for viewing!
"Moment Pooling: Gaining Performance and Interpretability Through Physics Inspired Product Structures", Talk, 10 April 2023, at APS April Meeting 2023. Minneapolis, Minesota, United States. Slides [PDF]
"Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics", Poster, 3 December 2022, at Machine Learning and the Physical Sciences. NeurIPS (Virtual). Poster [PNG]
★"Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics", Invited Talk, 22 Novemebr 2022, for ATLAS Jet/Etmiss. CERN (Virtual). Slides [PDF]
"Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics", Invited Seminar, 13 September 2022, for the UCI Physics Astro/Particle-ML Seminar series. University of Califoria, Irvine (Virtual). Slides [PDF]
"Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics", Talk, 7 July 2021, at ML4Jets2021. University of Heidelberg (Virtual). Slides [PDF]