My background is in mathematics, philosophy, and machine learning. I am interested in applying ideas from mechanistic interpretability and causal inference across the sciences.

Background

B.S. Mathematics, Philosophy — University of Massachusetts Amherst (2016–2020)

M.S. Computer Science — University of Massachusetts Amherst (2020–2022)

Machine Learning Engineer — Swarm Labs (2023–2024)

Machine Learning Engineer — Eluve Inc (2024–2026)

Visiting Researcher — University of Edinburgh, School of Informatics (2026–present)

Independent Researcher — Mechanistic Research

Selected Publications

• Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, Andrew McCallum. Knowledge Base Question Answering by Case-based Reasoning over Subgraphs. ICML 2022. [Proceedings]

Projects

• Elliot Tower. Mechanistic Validity: A Validity Theory, Research Methodology, and Evidence Standard for Mechanistic Claims. 2026. [Preprint]

• Elliot Tower. Mechanistic Views: An Atlas of Hidden Commitments and a Realism Criterion for Mechanistic Claims. 2026. [Preprint]

Current Research

1. Mechanistic Validity (Research Program)

Investigating mechanistic interpretability from the mathematical foundations, through the lenses of measurement theory and the philosophy of mechanisms. Applied across interpretability and AI safety.

2. Factorized circuits

Decomposing pretrained transformer weights into shared factor banks with sparse selectors to analyze information flow through the residual stream.