Rikab Gambhir
Theoretical Physicist
About Me
I am a physics PhD student at the Massachusetts Institute of Technology (MIT). My background is in physics, mathematics, data science, and applied sciences in engineering.
Interests
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Particle Physics
Quantum Field Theory
Jets and QCD
Beyond the Standard Model
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Data Science
Machine Learning
Optimal Transport
Education
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PhD Candidate in Physics, 2020-
Massachusetts Institute of Technology
Advisor: Jesse Thaler -
Bachelor of Science in Physics, Mathematics, and Applied Science 2016-2020
Rutgers University
summa cum laude, Highest Honors
Advisor: Stephen Schnetzer
RESEARCH & PUBLICATIONS
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?
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
Rikab Gambhir, Benjamin Nachman, and Jesse Thaler
9 May 2022
arXiv Preprint
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.
Search for bottom-type, vectorlike quark pair production in a fully hadronic final state in proton-proton collisions at s= 13 TeV
The CMS Collaboration
7 December 2020
Physical Review D 102 (11), 112004
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
Talks
Can You Hear the Shape of a Jet?
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
