Nate Woodward

I am a PhD student at the University of Wisconsin–Madison, where I develop machine learning techniques for theoretical physics. My current research focuses on symbolic reasoning methods for theoretical physics.

I graduated from MIT in 2025 with degrees in Physics and Mathematics. During my undergraduate studies I worked in Professor Phil Harris’s group and with the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), building ML tools for high‑energy physics. My experience spans geometric ML for jet physics, detector readout algorithms, and theoretical QFT calculations.

Recent Publications

• Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models (2025)

• Product Manifold Machine Learning for Physics (2024)

• NeurIPS ML4PS: Product Manifold Machine Learning for Physics (2024)

Recent Talks

• Bites of Foundation Models for Science: Product Manifold Machine Learning for Physics (2024)

• IAIFI Thematic Discussion on Representation and Manifold Learning: Product Manifold Machine Learning for Physics (2024)

• ML4Jets 2023: Hyperbolic Machine Learning for Physics (2023)