Rikab Gambhir

Theoretical Physicist

About Me

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.

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?

SHAPER: Can You Hear the Shape of a Jet?

Demba Ba, Akshunna S. Dogra, Rikab Gambhir, Abiy Tasissa, and Jesse Thaler

26 February 2023

[arXiv Preprint]

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.

You can install SHAPER for Python with the command pip install pyshaper

Bias and Priors in Machine Learning Calibrations for High Energy Physics

Rikab Gambhir, Benjamin Nachman, and Jesse Thaler

11 May 2022

[arXiv Preprint] [INSPIRE-HEP]

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.

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

Rikab Gambhir, Benjamin Nachman, and Jesse Thaler

9 May 2022

[arXiv Preprint] [INSPIRE-HEP]

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

PRESENTATIONS

Below is a list of all public presentations I have given, organized by topic. 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!

Can You Hear the Shape of a Jet?

  • Can You Hear the Shape of a Jet?", Internal Seminar, 17 March 2022, for the MIT CTP Graduate Seminar. MIT: Massachusetts, United States. Slides [PDF]

  • "Can You Hear the Shape of a Jet?", Talk, 4 Novemebr 2022, at ML4Jets2022. Rutgers University: New Jersey, United States. Slides [PDF]

  • "Can You Hear the Shape of a Jet?", Talk, 15 August 2022, at BOOST 2022. University of Hamburg: Hamburg, Germany. Slides [PDF]

  • "Can You Hear the Shape of a Jet? An IAIFI Story", Local Presentation, 11 August 2022, for IAIFI/NSF Review. MIT: Massachusetts, United States. Slides [PDF]

  • "Can You Hear the Shape of a Jet?", Talk, 10 April 2022, at APS April Meeting 2022. New York City: New York, United States. Slides [PDF]

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

  • "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]