Ari Brill

AI safety researcher & PhD astrophysicist

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I’m an independent AI safety researcher working to improve humanity’s scientific knowledge of advanced AI systems. My research currently focuses on creating mathematical and empirical models to study how AI systems develop internal representations of the world. My work is supported by a grant from the Long-Term Future Fund (EA Funds).

Previously, I was a NASA Postdoctoral Program Fellow at NASA Goddard Space Flight Center. At NASA, I used deep learning and statistical analysis to investigate high-energy extragalactic astrophysics, focusing on modeling the variability of gamma-ray emission powered by supermassive black holes.

I completed my PhD in Physics at Columbia in 2021. For my thesis, I studied extremely variable blazars using very-high-energy gamma-ray telescopes and developed experimental control software and analysis methods using deep neural networks to improve next-generation instruments. Before that, I completed a B.S. in Physics at Yale in 2015.

selected publications

  1. Neural Scaling Laws Rooted in the Data Distribution
    Ari Brill
    Preprint Dec 2024
  2. Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars
    Aryeh Brill
    In 2nd Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE) Feb 2023
  3. Variability Signatures of a Burst Process in Flaring Gamma-Ray Blazars
    A. Brill
    The Astrophysical Journal Sep 2022
  4. Variability and Spectral Characteristics of Three Flaring Gamma-Ray Quasars Observed by VERITAS and Fermi-LAT
    C. B. Adams, J. Batshoun, W. Benbow, and 63 more authors
    The Astrophysical Journal Jan 2022
  5. Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes
    Aryeh Brill, Qi Feng, T. Brian Humensky, and 3 more authors
    In 2019 New York Scientific Data Summit (NYSDS) Jan 2020