(new!) Prospective Students

Miles Cranmer

Assistant Professor of Data Intensive Science at University of Cambridge, working on AI for scientific discovery.

Polymathic AI: Foundation Models for Science

Polymathic AI is a research collaboration that develops foundation models for scientific data, aiming to usher in a new class of machine learning for science.

Rediscovering orbital mechanics with machine learning

Could we discover the law of gravitation without even knowing the masses of planets in the solar system? In this paper we show how.

Unsupervised Resource Allocation with GNNs

We show how to optimize resource allocation without knowing the true utility. We use this to learn a mock telescope observational survey from scratch.

Bayesian neural networks for planetary instability

We describe a Bayesian neural network architecture that can accurately learn to predict dynamical (chaotic) instability in compact planetary systems. The network demonstrates surprisingly robust generalization to 5-planet systems.

Discovering Symbolic Models from Deep Learning

We describe a technique for converting a deep learning model into an analytic equation, focusing on graph networks. We validate it on injected force laws and Hamiltonians, and then discover a new equation to accurately predict the overdensity of dark matter from its environment.

Lagrangian Neural Nets

We show how one can learn a Lagrangian from data with a Neural Network. Such an architecture conserves energy in a learned simulator without requiring canonical coordinates.

High-Performance Symbolic Regression in Python

A library for interpretable machine learning, using symbolic regression, in Python

High-Performance Symbolic Regression in Julia

The backend of PySR, written in pure Julia.

A tutorial on symbolic regression

I give a tutorial on symbolic regression using PySR, also covering symbolic distillation of neural networks.

An introduction to likelihood-free inference

I give an introduction to likelihood-free (simulation-based) inference for scientists.

Will artificial intelligence ever discover new laws of physics?

By Thomas Lewton. Algorithms can pore over astrophysical data to identify underlying equations. Now, physicists are trying to figure out how to imbue these “machine theorists” with the ability to find deeper laws of nature.

Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

By Charlie Wood. How can machine learning help us discover new theories of physics, rather than simply fitting data?

How Self-Driving Telescopes Could Transform Astronomy

By Ryan F. Mandelbaum. What if an autonomously operating telescope, free from human biases and complications, could find the solutions we’ve been missing?