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About/CV Hi, I'm Ibrahim

A Quick Introduction

I am a PhD candidate at the University of Toronto and Vector Institute advised by Prof. Yoni Kahn and doctoral researcher at Berkeley Lab (NERSC) advised by Wahid Bhimji and Benjamin Nachman. My research has been/is focused on using physics-inspired theory and physics data to understand neural network behavior, building foundation models that transfer across scientific domains, and developing AI tools to automate scientific discovery.

Education

  • 2025–Present

    PhD, Physics

    University of Toronto and Vector Institute

  • 2023–2025

    MSc, Physics

    University of Illinois Urbana-Champaign

  • 2019–2023

    B.S., Computational Physics
    B.A., Applied Mathematics & Philosophy

    Rice University

Experience

  • 2025–Present

    Doctoral Researcher

    Berkeley Lab

    Building foundation models for scientific point clouds/graphs for High Energy Physics, Cosmology and Molecular Dynamics at NERSC.

  • 2024–2025

    Research Geophysicist Intern

    ExxonMobil

    ML parameter estimation methods for 3D seismic inversion. Novel ML anomaly detection tools for 4D Seismic.

  • 2018–2023

    Research Geophysics & Software Intern

    Petroleum GeoServices

    ML methods for first break picking, seismic anomaly detection, cloud migration.

Awards

  • 2026 NSERC Doctoral Canadian Graduate Research Scholarship (CGRS D)
  • 2025 University of Toronto Connaught International Fellow
  • 2024 1st Place Higgs Uncertainty Challenge @ NeurIPS, CERN
  • 2023 3-Year UIUC Graduate College Fellowship
  • 2021 J & M Graham Physics Scholar
  • 2019 Shell Eco-Marathon
  • 2016 Eagle Scout

Teaching

  • 2026 TA and Lecturer, University of Toronto - Physics 2108/2109 “The Physics of Machine Learning”
  • 2025 TA, University of Toronto - Physics 252 “Thermal Physics”
  • 2024 TA, UIUC - Physics 413 “Optics Lab”

Select Papers

  1. Uncertainty Quantification from Ensemble Variance Scaling Laws in Deep Neural Networks
    Ibrahim Elsharkawy, et al.
    Machine Learning: Science and Technology, vol. 6, no. 3, 2025
    Paper
  2. Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation
    Ibrahim Elsharkawy and Yonatan Kahn
    arXiv preprint, 2025
    arXiv
  3. OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers
    Ibrahim Elsharkawy, et al.
    arXiv preprint, 2026
    arXiv
  4. Generative Models on Phase Space
    Zachary Bogorad, Ibrahim Elsharkawy, et al.
    arXiv preprint, 2026
    arXiv
  5. FAIR Universe HiggsML Uncertainty Dataset and Competition
    Wahid Bhimji, et al.
    NeurIPS 2025, Dataset and Competition Track
  6. OmniCosmos: Transferring Particle Physics Knowledge across the Cosmos
    Vinicius Mikuni, et al.
    arXiv preprint, 2025
    arXiv

Select Talks & Posters

  • Invited Talk
    1st Place Competition Milestone (Ensembles and Uncertainty Quantification)
    The Challenge of Handling Uncertainties in Fundamental Science @ NeurIPS 2024
  • Invited Talk
    Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation
    CERN 7th Inter-Experimental LHC Machine Learning Workshop, 2025
  • Poster
    FAIR Universe HiggsML Uncertainty Dataset and Competition
    NeurIPS 2025, Dataset and Competition Track
  • Poster
    Uncertainty Quantification from Scaling Laws in Deep Neural Networks
    Machine Learning and the Physical Sciences Workshop @ NeurIPS 2024
  • Poster
    Pre-Training For Science: A study on Foundation Model Training Objectives
    Stanford Center of Decoding the Universe Forum, 2025