Hey, I'm Tommy! I'm broadly interested in topics that improve our understanding of machine learning systems and their generalization capabilities including:

• Interpretability: mechanistic interpretability, representation analysis, attribution methods
• Open-ended learning: self-play, unsupervised RL
• Mathematical foundations of ML: geometry of learning, optimization techniques, deep learning theory

Email: tommyhe6@gmail.com
LinkedIn: https://linkedin.com/in/tommyhe6
Github: https://github.com/tommyhe6

Papers

• A Predictive Law for On-Policy Self-Distillation From World Feedback (2026)
  Tommy He, Jerome Sieber*, Matteo Saponati*
  ICML 2026 RLxF Workshop

Writings

• Overview of Isogeny Based Cryptography
• Security is not Well-Defined
• Theory of Machine Learning
• Introduction to Algebraic Geometry