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Faculty of Mathematics

 

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

Machine Learning with Physics Knowledge for Prediction: A Survey
J Watson, C Song, O Weeger, T Gruner, AT Le, K Pompetzki, A Hendawy, O Arenz, W Trojak, M Cranmer, C D’eramo, F Bülow, T Goyal, J Peters, MW Hoffman
– Transactions on Machine Learning Research
(2025)
2025-May,
Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
P Wyder, J Goldfeder, A Yermakov, Y Zhao, S Riva, J Williams, D Zoro, A Rude, M Tomasetto, J Germany, J Bakarji, G Maierhofer, M Cranmer, N Kutz
– Advances in Neural Information Processing Systems 38
(2025)
38,
188884
(doi: 10.52202/085713-5677)
xVal: A Continuous Numerical Tokenization for Scientific Language Models
S Golkar, M Pettee, M Eickenberg, A Bietti, M Cranmer, G Krawezik, F Lanusse, M McCabe, R Ohana, L Parker, BR-S Blancard, T Tesileanu, K Cho, S Ho
(2024)
(doi: 10.48550/arxiv.2310.02989)
Multiple Physics Pretraining for Physical Surrogate Models
M McCabe, BR-S Blancard, LH Parker, R Ohana, M Cranmer, A Bietti, M Eickenberg, S Golkar, G Krawezik, F Lanusse, M Pettee, T Tesileanu, K Cho, S Ho
(2024)
(doi: 10.48550/arxiv.2310.02994)
The Multimodal Universe: 100 TB of Machine Learning Ready Astronomical Data
E Angeloudi, J Audenaert, M Bowles, BM Boyd, D Chemaly, B Cherinka, I Ciucă, M Cranmer, A Do, M Grayling, EE Hayes, T Hehir, S Ho, M Huertas-Company, KG Iyer, M Jablonska, F Lanusse, HW Leung, K Mandel, JR Martínez-Galarza, P Melchior, L Meyer, LH Parker, H Qu, J Shen et al.
– Research Notes of the AAS
(2024)
8,
301
(doi: 10.3847/2515-5172/ad9a63)
The ones that got away: chemical tagging of globular cluster-origin stars with Gaia BP/RP spectra
SG Kane, V Belokurov, M Cranmer, S Monty, H Zhang, A Ardern-Arentsen
– Monthly Notices of the Royal Astronomical Society
(2024)
536,
stae2689
(doi: 10.1093/mnras/stae2689)
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
The Multimodal Universe Collaboration, J Audenaert, M Bowles, BM Boyd, D Chemaly, B Cherinka, I Ciucă, M Cranmer, A Do, M Grayling, EE Hayes, T Hehir, S Ho, M Huertas-Company, KG Iyer, M Jablonska, F Lanusse, HW Leung, K Mandel, JR Martínez-Galarza, P Melchior, L Meyer, LH Parker, H Qu, J Shen et al.
(2024)
(doi: 10.48550/arxiv.2412.02527)
(link to publication)
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
R Ohana, M McCabe, L Meyer, R Morel, FJ Agocs, M Beneitez, M Berger, B Burkhart, K Burns, SB Dalziel, DB Fielding, D Fortunato, JA Goldberg, K Hirashima, Y-F Jiang, RR Kerswell, S Maddu, J Miller, P Mukhopadhyay, SS Nixon, J Shen, R Watteaux, BR-S Blancard, F Rozet, LH Parker et al.
(2024)
(link to publication)
Accelerating Giant-impact Simulations with Machine Learning
C Lammers, M Cranmer, S Hadden, S Ho, N Murray, D Tamayo
– The Astrophysical Journal
(2024)
975,
228
(doi: 10.3847/1538-4357/ad7fe5)
Accelerating Giant Impact Simulations with Machine Learning
C Lammers, M Cranmer, S Hadden, S Ho, N Murray, D Tamayo
(2024)
(doi: 10.48550/arxiv.2408.08873)
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Research Group

Relativity and Gravitation

Email

mc2473@cam.ac.uk

Room

B2.17

Study at Cambridge

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Research at Cambridge