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Relativity and Gravitation Group

Dr Miles Cranmer

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

Multiple Physics Pretraining for Spatiotemporal Surrogate Models
M McCabe, BR-S Blancard, L Parker, R Ohana, M Cranmer, A Bietti, M Eickenberg, S Golkar, G Krawezik, F Lanusse, M Pettee, T Tesileanu, K Cho, S Ho
– Advances in Neural Information Processing Systems
(2024)
37,
119301
(doi: 10.52202/079017-3791)
Workshop Summary: Exoplanet Orbits and Dynamics
A-L Maire, L Delrez, FJ Pozuelos, J Becker, N Espinoza, J Lillo-Box, A Revol, O Absil, E Agol, JM Almenara, G Anglada-Escudé, H Beust, S Blunt, E Bolmont, M Bonavita, W Brandner, GM Brandt, TD Brandt, G Brown, CC Mitjans, C Charalambous, G Chauvin, ACM Correia, M Cranmer, D Defrère, M Deleuil, B-O Demory, RJ De Rosa, S Desidera, M Dévora-Pajares, RF Díaz, C Do Ó, E Ducrot, TJ Dupuy, R Ferrer-Chávez, C Fontanive, M Gillon, C Giuppone, L Gkouvelis, G de Oliveira Gomes, SRA Gomes, MN Günther, S Hadden, Y Han, DM Hernandez, E Jehin, SR Kane, P Kervella, F Kiefer, QM Konopacky, M Langlois, B Lanssens, C Lazzoni, M Lendl, Y Li, A-S Libert, F Lovos, RG Miculán, Z Murray, E Pallé, H Rein, L Rodet, A Roisin, J Sahlmann, R Siverd, M Stalport, JC Suárez, D Tamayo, J Teyssandier, A Thuillier, M Timmermans, AHMJ Triaud, T Trifonov, EFS Valente, V Van Grootel, M Vasist, JJ Wang, MC Wyatt, J Xuan, S Young, NT Zimmerman
– 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
C Pedersen, T Tesileanu, T Wu, S Golkar, M Cranmer, Z Zhang, S Ho
(2023)
(doi: 10.48550/arxiv.2309.16645)
Rediscovering orbital mechanics with machine learning
P Lemos, N Jeffrey, M Cranmer, S Ho, P Battaglia
– 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
JW Park, S Birrer, M Ueland, M Cranmer, A Agnello, S Wagner-Carena, PJ Marshall, A Roodman, TLDES Collaboration
– Astrophysical Journal
(2023)
953,
178
(doi: 10.3847/1538-4357/acdc25)
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
FO de Franca, M Virgolin, M Kommenda, MS Majumder, M Cranmer, G Espada, L Ingelse, A Fonseca, M Landajuela, B Petersen, R Glatt, N Mundhenk, CS Lee, JD Hochhalter, DL Randall, P Kamienny, H Zhang, G Dick, A Simon, B Burlacu, J Kasak, M Machado, C Wilstrup, WG La Cava
(2023)
(doi: 10.48550/arxiv.2304.01117)
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
M Cranmer
(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
D Wadekar, L Thiele, JC Hill, S Pandey, F Villaescusa-Navarro, DN Spergel, M Cranmer, D Nagai, D Anglés-Alcázar, S Ho, L Hernquist
– Monthly Notices of the Royal Astronomical Society
(2023)
522,
2628
(doi: 10.1093/mnras/stad1128)
The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
D Wadekar, L Thiele, JC Hill, S Pandey, F Villaescusa-Navarro, DN Spergel, M Cranmer, D Nagai, D Anglés-Alcázar, S Ho, L Hernquist
(2023)
(doi: 10.48550/arxiv.2209.02075)
Charting Galactic Accelerations with Stellar Streams and Machine Learning
J Nibauer, V Belokurov, M Cranmer, J Goodman, S Ho
(2023)
(doi: 10.48550/arxiv.2205.11767)
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Research Group

Relativity and Gravitation

Email

mc2473@cam.ac.uk

Room

B2.17

Study at Cambridge

About the University

Research at Cambridge