Neurosymbolic programming for science
A 2022 position paper, presented at the NeurIPS 2022 AI for Science workshop, on combining neural learning with symbolic programs. It aims to give researchers interpretable machine-learning tools.
Consulting service
Scientific AI Research Advisory
Research planning and guidance for AI-assisted model discovery, from experimental data to equations, programs, or hypotheses that domain experts can examine.
Scope
What this advisory can cover
Translate a scientific question into a data and modeling plan
Explore symbolic regression and equation-synthesis methods
Combine data with domain knowledge and explicit constraints
Compare candidate models for accuracy and interpretability
Design review steps for researchers assessing generated hypotheses
Document the data, methods, and assumptions behind each result
Related work
Published work related to this advisory area
Selected publications, talks, and projects related to this area. Each link opens the work and its original source.
InceptionSR: Recursive Symbolic Regression for Equation Synthesis
InceptionSR is a symbolic-regression method that freezes promising partial equations and reuses them as features in later search rounds. It is tested on planetary-system instability and a triangle-area formula.
Symbolic regression with a learned concept library
This NeurIPS 2024 paper introduces LaSR, a symbolic-regression method in which a large language model finds reusable concepts in strong candidate equations. On the Feynman benchmark it recovered more target equations than the methods it was compared with.
LLMs as Post-hoc Auditors of Physiological Plausibility in Symbolic Regression: A Clinician-Evaluated Case Study
A 2026 preprint tests whether large language models can help clinicians compare symbolic-regression equations for body-fat estimation. A panel of clinicians rated the outputs.