Nicholas A. Gabriel

CV | Google Scholar | GitHub | LinkedIn

About Me

I am a recent Physics PhD graduate from George Washington University. Previously I have used a range of AI/ML techniques: model architecture design, foundation model training, parameter-efficient fine-tuning, RAG, Agents, MLOps (cloud computing, containers, orchestration), graph ML, and large-scale data collection. I am interested in AI/ML Scientist and Engineering roles for a wide range of applications and domains.

Skills: Python (10+ years), PyTorch (5+ years), Transformers (4+ years). See my CV for extended list of projects and specific skills.

In my spare time I enjoy cycling, making espresso, and watching train videos.

contact: nickgabriel8[at]gmail[dot]com

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

• Yale University (Lu group meeting, 45m presentation) [slides]
  "Message Passing Neural Operators for Many-Body Complex Systems", 8/13/2023.
• 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.