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
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
– 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)
The SZ flux-mass (Y–M) relation at low-halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
– Monthly Notices of the Royal Astronomical Society
(2023)
522,
2628
(doi: 10.1093/mnras/stad1128)
Charting Galactic Accelerations with Stellar Streams and Machine Learning
(2023)
(doi: 10.48550/arxiv.2205.11767)
Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev-Zeldovich flux-mass scatter
– Proc Natl Acad Sci U S A
(2023)
120,
e2202074120
(doi: 10.1073/pnas.2202074120)
Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter
(2023)
(doi: 10.48550/arxiv.2201.01305)
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