Brandon Pries

About Me

I grew up in Michigan. I did my undergrad work at Michigan State University (MSU). I did research on neutrinos with the IceCube Collaboration, including projects on event reconstruction using different neural network architectures and a statistics project on detecting signals of neutrinos from dark matter annihilation. I was also an Undergraduate Learning Assistant (ULA) for the intro astronomy courses for three semesters.

I'm now doing my graduate work at Georgia Institute of Technology (Georgia Tech / GT). I work in Dr. John Wise's group studying supermassive black holes in the early universe. I'm currently developing a machine learning model to learn what conditions are relevant for seeding these supermassive black holes in cosmological simulations. I've been a Graduate Teaching Assistant (GTA) for five semesters in the intro physics courses for non-majors.

Brief CV

Full CV

Education

Georgia Institute of Technology, Atlanta, GA: August 2022 – Present

• Doctor of Philosophy, Physics: Expected December 2027
• Doctoral Minor in Higher Education

Michigan State University, East Lansing, MI: August 2018 – May 2022

• Bachelor's of Science, Astrophysics: May 7, 2022
• Minor in Mathematics
• Minor in Computational Mathematics, Science, and Engineering (CMSE)
• Minor in Data Science

Professional Appointments

• Graduate Research Assistant (GRA), Georgia Tech: May 2024 – Present
• Graduate Teaching Assistant (GTA), Georgia Tech: August 2022 – May 2024
• Researcher, MSU: May 2022 – Present
• Undergraduate Learning Assistant (ULA), MSU: January 2021 – May 2021; August 2021 – May 2022
• Undergraduate Research Assistant, MSU: May 2019 – August 2019; May 2020 – May 2022
• Professorial Assistant (PA), Honors College, MSU: August 2018 – May 2019; August 2019 – May 2020

Research Experience

Wise group, Georgia Tech: August 2023 – Present

• Direct-Collapse Black Hole (DCBH) Formation
  1. Predicting formation of DCBHs in dark matter halos using support vector machines (SVMs)
  2. Optimizing SVM hyperparameters using grid-search algorithm
  3. Measuring importance of predictive features for classification of DCBH-hosting halos

IceCube Collaboration, MSU: August 2018 – Present

• Neutrinos from Dark Matter Annihilation
  1. Processing 7 years of IceCube data to use with neutrino spectra from dark matter annihilation
  2. Generating custom probability distribution functions (PDFs) to calculate IceCube sensitivities to annihilation spectra
  3. Tracking progress with analysis Wikipedia page and analysis GitHub repository
• Recurrent Neural Network (RNN) Event Reconstruction
  1. Reconstructed neutrino events using RNNs for orders-of-magnitude increase in reconstruction speed
  2. Gathered and processed approximately 2 million neutrino events as data for use in RNN research
  3. Tracked progress with analysis GitHub repository
• Convolutional Neural Network (CNN) Event Reconstruction
  1. Optimized structure of CNNs using grid-search algorithm
  2. Explored effects of 5 different loss functions on CNN regression problems for event reconstruction
  3. Investigated methods for multivariate regression with CNNs

Manuscripts Under Review

1. E. Mone, B. Pries, J. H. Wise, & S. Ferrans. "Beyond the Goldilocks Zone: Identifying Critical Features in Massive Black Hole Formation." arXiv: 2412.08829, December 12, 2024. Submitted to ApJ.

Manuscripts in Preparation

1. R. Abbasi et al. (IceCube Collaboration, incl. B. Pries). "Limits on GeV-scale WIMP Annihilation in Dwarf Spheroidals with IceCube DeepCore." In prep.

Selected Presentations

1. B. Pries. "Identifying Direct Collapse Blac Hole Sites in Cosmological Simulations." The Formation and Early Evolution of Supermassive Black Holes, Baltimore, MD, November 20, 2024.
2. B. Pries. "Sensitivities to WIMP Annihilation Cross Sections with IceCube DeepCore." American Physical Society (APS) April Meeting, Sacramento, CA (virtual), April 5, 2024.
3. B. Pries. "Sensitivities to Low-Mass WIMP Annihilation Cross Sections with IceCube Neutrinos." American Physical Society (APS) April Meeting, Minneapolis, MN (virtual), April 24, 2023.
4. B. Pries. "IceCube Search for Low-Mass WIMP Annihilation in Dwarf Galaxies." Astronomy Seminar, MSU Department of Physics and Astronomy, April 20, 2022.
5. B. Pries & N. Willey. "Recurrent Neural Networks as a Tool for IceCube-Upgrade Reconstructions." Student Machine Learning Initiative, Brown University (virtual), October 5, 2021.
6. B. Pries. "IceCube-Upgrade Reconstructions using Recurrent Neural Networks." 2021 American Physical Society Division of Particles and Fields (APS DPF) Meeting, Florida State University (virtual), July 14, 2021.

Teaching Experience

Head Teaching Assistant (Head TA), Georgia Tech: Summer 2023 – Spring 2024

• PHYS 2211 – Intro Physics I: Spring 2024
• PHYS 2211 – Intro Physics I: Fall 2023
• PHYS 2211 – Intro Physics I: Summer 2023

Graduate Teaching Assistant (GTA), Georgia Tech: Fall 2022 – Spring 2023

• PHYS 2211 – Intro Physics I: Spring 2023
• PHYS 2211 – Intro Physics I: Fall 2022

Undergraduate Learning Assistant (ULA), MSU: Spring 2021; Fall 2021 – Spring 2022

• AST 208 – Planets and Telescopes: Spring 2022
• AST 207 – The Science of Astronomy: Fall 2021
• AST 208 – Planets and Telescopes: Spring 2021

Honors, Scholarships, and Awards

• Online Head TA of the Year, Georgia Tech: April 17, 2024
  • $500 award
• Online Head TA of the Year, School of Physics, Georgia Tech: March 7, 2024
• Thomas H. Osgood Award, Department of Physics and Astronomy, MSU: April 28, 2022
  • Oustanding Undergraduate Senior
• Outstanding ULA Award, Department of Physics and Astronomy, MSU: April 22, 2021; April 28, 2022
  • Upper-Level Physics/Astronomy Course
• Honors College, MSU: Fall 2018 – Spring 2024
• Professorial Assistantship (PA), Honors College, MSU: Fall 2018 – Spring 2019; Fall 2019 – Spring 2020
  • Research scholarship, ~$8,700 award