Bio
I am an incoming PhD student at Stanford University and a theoretical high-energy physicist in training, aiming to combine quantum field theory and machine learning to tackle fundamental questions in elementary physics.
My existing research with Profs. Alfredo Gurrola, Raghav Elayavalli, and Andres Florez revolves around enhancing the discovery potential of the Large Hadron Collider by developing novel theoretical approaches and data analysis methods.
In the past I have also dabbled with numerical relativitiy, condensed matter physics, geometric deep learning, optimal transport, and observational astronomy.
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Research
Interests
My research in theoretical high-energy physics aims to drive the search for new phenomena beyond the Standard Model while also deepening our understanding of the Standard Model itself, especially in QCD, jet physics, and the quark-gluon plasma. I often blend fundamental quantum field theory calculations with modern machine learning techniques including geometric deep learning and optimal transport.
During my undergraduate studies, I was advised by Profs. Alfredo Gurrola, Raghav Elayavalli, and Andres Florez.
For a more detailed summary of my projects, please consult my CV (Dec. 2024).
Publications
The most up-to-date list of my publications can be found on InspireHEP. You can also find links to (preprints of) my papers on my Google Scholar profile, as well as on the Harvard ADS abstract service or directly on the arXiv.
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Conference Presentations
2025
Model-Agnostic Tagging of Quenched Jets in Heavy-ion Collisions.
Quark Matter: XXXI International Conference on Ultra-relativistic Nucleus-Nucleus Collisions, Goethe University, Frankfurt, Main, Germany.
PartonFlow: Reconstructing Parton-Level Jets after Hadronization.
Hot Jets: Advancing the Understanding of High Temperature QCD with Jets, Loomis Lab, Urbana Champaign, IL, United States.
2024
Generative Neural Networks for Reconstructing Parton-Level Jet Showers after Hadronization.
Machine Learning for Jet Physics (ML4Jets), LPNHE, Paris, France.
Probing the Supersymmetric Standard Model at the Large Hadron Collider through Vector Boson Fusion Processes and Machine Learning.
BOOST 2024: 16th International Workshop on Boosted Object Phenomenology, Reconstruction, Measurements, and Searches at Colliders, INFN, Genova, Italy.
Graph Neural Networks for Inverting Hadronization.
Fall Meeting of the APS Division of Nuclear Physics (DNP), MIT, Cambridge, MA.
A Herwig7 Underlying Event Tune for 200 GeV RHIC and EIC Energies.
Annual RHIC & AGS Users’ Meeting, Brookhaven National Lab, Upton, NY.
Probing a GeV-scale Scalar Boson and a TeV-scale Vector-like Quark in the U(1)T3R BSM Extension at the Large Hadron Collider using Machine Learning.
Phenomenology Symposium × APS Division of Particles & Fields (DPF) Meeting, University of Pittsburgh, PA.
2023
Probing a GeV-scale Scalar Boson in Association with a TeV-scale Vector-like Quark in the U(1)T3R BSM Extension at the LHC using Machine Learning.
31st International Symposium on Lepton Photon Interactions at High Energies, Monash University, Melbourne, Australia.
Probing a MeV-scale Scalar Boson and a TeV-Scale Vector-like Quark in the U(1)T3R BSM Extension from gg Fusion, qq Fusion at the LHC using Machine Learning.
XII International Conference on New Frontiers in Physics, Orthodox Academy of Crete, Greece.
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