Nicholas A. Gabriel

Resume | Google Scholar | GitHub | LinkedIn

About Me

I am a PhD candidate in the Physics Department at The George Washington University. I study complex systems using informed AI/ML techniques with a particular emphasis on large-scale graphs and effective dynamics. A major theme of my research is the interplay between physics and AI/ML: developing novel architectures for solving inverse problems in complex systems physics, and designing neural analogs of techniques from network science and statistical physics.

In my spare time I enjoy cycling, making espresso, watching train videos, and staring into the sun while wearing NPR hats (see photo evidence above).

Publications

• Connecting the geometry and dynamics of many-body complex systems with message passing neural operators.
  Nicholas A. Gabriel, Neil F. Johnson, George Em Karniadakis
  arXiv preprint, 2025.
• Inductive detection of Influence Operations via Graph Learning.
  Nicholas A. Gabriel, David A. Broniatowski, Neil F. Johnson
  Scientific Reports, 2023.
• Using Neural Architectures to Model Complex Dynamical Systems.
  Nicholas A. Gabriel, Neil F. Johnson
  Advances in Artificial Intelligence and Machine Learning, 2022.
• Online hate network spreads malicious COVID-19 content outside the control of individual social media platforms.
  Nicolas Velasquez, Rhys Leahy, Nicholas Restrepo, Yonatan Lupu, Richard Sear, Nicholas Gabriel, Om Jha, Beth Goldberg, Neil Johnson
  Scientific Reports, 2021.
• The online competition between pro- and anti-vaccination views.
  Neil Johnson, Nicolas Velásquez, Nicholas Restrepo, Rhys Leahy, Nicholas Gabriel, Sara El Oud, Minzhang Zheng, Pedro Manrique, Stefan Wuchty, Yonatan Lupu
  Nature, 2020.
• Quantifying COVID-19 content in the online health opinion war using machine learning.
  Richard F Sear, Nicolás Velásquez, Rhys Leahy, Nicholas Restrepo, Sara El Oud, Nicholas Gabriel, Yonatan Lupu, Neil Johnson
  IEEE Access, 2020.

Presentations

• The George Wasington University (ENIGMA seminar, 45m presentation) [slides]
  "Multiscale Operator Learning for complex social systems", 10/4/2023.
• Brown University (CRUNCH group meeting, 40m presentation) [slides]
  "Multiscale Operator Learning for complex social systems", 9/15/2023.
• IC2S2 2022 (Conference talk, 15m presentation) [slides]
  "Automated Detection of Information Operations Using Graph Neural Networks", 7/21/2022.
• Brookhaven National Laboratory (PROSPECT group meeting, 20m presentation) [report]
  "Mass calibration for PROSPECT", 8/10/2016.