Assistant Professor in Data Intensive Science in DAMTP and the IoA, working on AI for scientific discovery.
Research: Google Scholar
Group page: astroautomata.com
[quanta magazine]
Publications
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning.
– Advances in Neural Information Processing Systems 37
(2024)
37,
44989
(doi: 10.52202/079017-1430)
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data
– ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 37 (NEURIPS 2024)
(2024)
37,
Multiple Physics Pretraining for Spatiotemporal Surrogate Models
– Advances in Neural Information Processing Systems
(2024)
37,
119301
(doi: 10.52202/079017-3791)
Workshop Summary: Exoplanet Orbits and Dynamics
– Publications of the Astronomical Society of the Pacific
(2023)
135,
106001
(doi: 10.1088/1538-3873/acff88)
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures
(2023)
(doi: 10.48550/arxiv.2309.16645)
Rediscovering orbital mechanics with machine learning
– Machine Learning: Science and Technology
(2023)
4,
045002
(doi: 10.1088/2632-2153/acfa63)
Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
– The Astrophysical Journal
(2023)
953,
178
(doi: 10.3847/1538-4357/acdc25)
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
(2023)
(doi: 10.48550/arxiv.2304.01117)
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
(2023)
(doi: 10.48550/arxiv.2305.01582)
The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
(2023)
(doi: 10.48550/arxiv.2209.02075)
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