Ali Shehper

I am a researcher specializing in AI, particularly in reinforcement learning algorithms for long-horizon, ultra-sparse-reward problems and the interpretability of AI systems. I received my PhD in Physics from The University of Texas at Austin, under the guidance of Jacques Distler and Andrew Neitkze, and I have an ongoing collaboration with Sergei Gukov at Caltech.

I am passionate about solving complex AI problems and exploring their implications for both theory and practice. Feel free to browse my work, and reach out for potential collaborations!

AI Papers:

• What makes math problems hard for reinforcement learning: a case study,
  A. Shehper, A. M. Medina-Mardones, B. Lewandowski, A. Gruen, P. Kucharski, S. Gukov.
  Investigating reinforcement learning challenges in mathematical problem-solving.

Code:

• AC-Solver:
  A Python library for tackling long-horizon, ultra-sparse-reward RL environments, designed to accompany our case study.

• Sparse-Dictionary-Learning:
  An open-source implementation of Anthropic’s Towards Monosemanticity: Decomposing Language Models with Dictionary Learning.

• Neural Scaling Laws:
  An implementation of Scaling Laws for Neural Language Models, along with results from An Empirical Model of Large-Batch Training.

• Language Model Feature Browser:
  A visualizer for features learned by a 1-layer Language Model, with the GitHub Repository.

Physics / Math Papers:

• PhD Thesis: Aspects of Supersymmetric and Topological Quantum Field Theories.

• Nonabelian Twists of the D4 Theory:
  J. Distler, B. Ergun, A. Shehper (co-primary contributor, names in alphabetical order).

• Symmetries of 2d TQFTs and Equivariant Verlinde Formulae for General Groups:
  S. Gukov, D. Pei, C. Reid, A. Shehper (primary contributor, names in alphabetical order).

• Distinguishing 4d N=2 SCFTs:
  J. Distler, B. Ergun, A. Shehper (co-primary contributor, names in alphabetical order).

• Deformations of surface defect moduli spaces:
  A. Neitzke, A. Shehper (primary contributor, names in alphabetical order).

Contact and Collaboration

I am always open to discussions on reinforcement learning, AI interpretability, and related areas of physics and mathematics. Feel free to reach out for potential collaborations, research opportunities, or simply to connect.

Google Scholar / GitHub / Twitter / LinkedIn