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

  • Particle Physics

    • Quantum Field Theory

    • Jets and QCD

    • Beyond the Standard Model

  • Data Science

    • Machine Learning

    • Optimal Transport

Education

  • 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

See More [CV]

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?

APS April 2022: Data Analysis, AI, and ML I

New York, NY, 10 April 2022

Slides [PDF]

Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics

ML4Jets2021: Regression, Calibration, and Fast Inference

Remote Talk, 7 July 2021

Slides [PDF]