Astro Automata
Automating Scientific Discovery
Our research group at the University of Cambridge develops and applies AI techniques across the physical sciences. Our work spans from foundational ML methods to scientific applications in astrophysics, cosmology, mathematics, fluid dynamics, and physics.
Our Research
Foundation Models for Science
Along with our collaborators at Flatiron Institute, we form one of the nodes of PolymathicAI, an international collaboration building large-scale foundation models for science, pretrained on diverse numerical datasets. Just as large language models have transformed natural language processing, we aim to build large "quantitative" models that transfer concepts across scientific disciplines.
Interpretable ML for Science
At the other end of the spectrum, we drive the development of machine learning methods which yield clear and interpretable scientific insights, lifting the black box predictions of deep learning. Our work includes the popular open-source framework PySR for symbolic regression, among significant work on interpretability of deep neural networks in the context of physics.
Scientific Discovery across Physics and Astronomy
We simultaneously apply our AI techniques to targeted problems across the physical sciences. Our scientific interests span a variety of topics (often student-driven), including turbulence modeling, planetary dynamics, oceanography, cosmological inference, exoplanet characterization, among many other areas.
Our Team
Miles Cranmer
PI
Assistant Professor, DAMTP & IoA
Topics: all
Meatball
Co-PI
Catbridge
Topics: Sleeping
Payel Mukhopadhyay
Postdoctoral Fellow
DAMTP
Topics: PolymathicAI, Deep Learning for Simulations
Tom Hehir
PhD Student
IoA
Topics: PolymathicAI, Galaxy Formation
Rio Fear
PhD Student
DAMTP
Topics: PolymathicAI, Mechanistic Interpretability
Júlia Laguna Miralles
PhD Student
IoA
Co-advised with Vasily Belokurov
Topics: Quasars, Time Series Models
Hadi Sotoudeh
PhD Student
IoA
Co-advised with Neil Lawrence
Topics: PolymathicAI, Galaxy Formation, Deep Learning for Simulations
Ed Stevenson
PhD Student
IoA
Co-advised with Oliver Shorttle
Topics: Exoplanets, Deep Learning for Simulations
Luis Carretero
Masters Student
(external) ETH Zurich
Topics: Symbolic Regression
Helen Shao
Masters Student
Cambridge
Co-advised with Blake Sherwin
Topics: CMB Foregrounds, Diffusion Models
Adil Soubki
Incoming PhD Student
IoA
Topics: (TBD; likely on ML-Accelerated Discovery and applications in Astrophysics)
Rachel Zhang
Incoming PhD Student
DAMTP
Topics: (TBD; likely related to PolymathicAI and broader AI for Science)
Recent Publications
Discovering symbolic models from deep learning with inductive biases
We develop a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases. We focus on Graph...
Lagrangian neural networks
Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, suc...
The CHIME fast radio burst project: system overview
The Canadian Hydrogen Intensity Mapping Experiment (CHIME) is a novel transit radio telescope operating across the 400-800-MHz band. CHIME is compr...
Our Software
High-Performance Symbolic Regression in Python and Julia
Code for "Discovering Symbolic Models from Deep Learning with Inductive Biases"