Benjamin Thorne

Benjamin Thorne

Graduate Student Researcher

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location of Benjamin ThorneSan Francisco, California, United States

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  • Timeline

  • About me

    Machine Learning Engineer, Physicist

  • Education

    • University of Oxford

      2015 - 2019
      Doctor of Philosophy - PhD Astrophysics
    • University of Oxford

      2011 - 2015
      Master’s Degree MPhys First
  • Experience

    • University of Oxford, Department of Physics

      Oct 2015 - Jun 2019
      Graduate Student Researcher
    • The University of Tokyo

      Aug 2016 - Sept 2017
      Graduate Student Researcher

      I was the inaugural Kavli IPMU - University of Oxford Fellow in astrophysics. This fellowship allows for a DPhil student in astrophysics at Oxford to spend a year or more at the IPMU, conducting their research under the supervision of a local professor. I worked with Professor Nobuhiko Katayama.My work at IPMU was mainly analytical. I derived theoretical predictions for astrophysical observables in a parity-violating cosmological model, and forecasted the constraining power of the LISA laser interferometer, and LiteBIRD satellites for such a model. Show less

    • Princeton University

      Sept 2017 - Jul 2019
      Graduate Student Researcher

      At Princeton I was advised by Professor Jo Dunkley, and worked on several research projects related to analysis of astronomical datasets:• Developed widely used Python package, pysm, which simulates cosmological data. This software now underpins Galactic and extragalactic sky simulations in most cosmic microwave background experiments. • Developed Bayesian pixel-fitting Python code to be applied to multifrequency cosmic microwave background datasets, and applied it to forecast the impact of spurious signals from foreground contamination on science results from the upcoming Simons Observatory. Show less

    • University of California, Davis

      Jul 2019 - Dec 2022
      Postdoctoral Scholar

      • Developed key software pipeline for analysis of CMB data collected with the South Pole Telescope and BICEP / Keck array. This pipeline implements MCMC sampling of a large hierarchical Bayesian model for CMB images, and makes use of hardware acceleration and advanced statistical techniques. • Developed machine learning models of non-Gaussian foreground fields. Built a variational autoencoder model of Galactic emission for use in Bayesian inverse tasks, and developed new non-Gaussian analytical models.• Co-developed v3.0 of the PySM Python package. This included targeted development for performance in HPC environments, as well as the implementation of new models supporting. Show less

    • Berkeley Lab

      Apr 2023 - Oct 2023
      Machine Learning Engineer
    • Atomic Industries

      Oct 2023 - now
      Senior Machine Learning Engineer
  • Licenses & Certifications

    • Machine Learning Specialization

      Coursera
      Aug 2022
      View certificate certificate
    • Probabilistic Deep Learning with TensorFlow 2

      Coursera
      Aug 2022
      View certificate certificate
    • Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

      Coursera
      Aug 2020
      View certificate certificate