Research

I'm broadly interested in machine learning theory, in particular the design and analysis of statistically and computationally efficient algorithms. I'm especially interested in the simplest optimal algorithms, and learning in structured settings.

I work on statistical learning theory with Nikita Zhivotovskiy in Berkeley's Statistical Machine Learning group, and on deep learning theory with Michael DeWeese at BAIR.

I also work on computational nuclear physics at LLNL.

Google Scholar: link. ORCID: 0009-0004-1252-1679.

(Short writeups of some of this work are available here.)

Publications

(\(\alpha\beta\)) denotes alphabetical ordering, * denotes equal contribution.

Majority-of-Three is Optimal

Divit Rawal, N. Zhivotovskiy (\(\alpha\beta\))

Preprint. Under review. [arXiv] / (tldr)

A Theory of Saddle Escape in Deep Nonlinear Networks

Divit Rawal, M. R. DeWeese

Preprint. Under review. [arXiv]

Rao-Blackwellized Score Matching on Manifolds

Divit Rawal

ICML 2026, Workshop on Structured Probabilistic Inference and Generative Modeling. [arXiv] / (tldr)

ALPHANSO: Open-Source Modeling of (\(\alpha\),n) Neutron Source Terms

Divit Rawal, A. J. Nelson, W. Zywiec, D. Siefman

Nuclear Instruments and Methods in Physics Research Section A. [doi]

American Nuclear Society Student Conference, 2026. (Best Paper in Mathematics, Computation, and AI Applications).

INMM Annual Meeting 2026.

Invited talks at LBNL, LLNL, UT Knoxville.