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Department of Applied Mathematics and Theoretical Physics

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

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)
AION-1: Omnimodal Foundation Model for Astronomical Sciences
L Parker, F Lanusse, J Shen, O Liu, T Hehir, L Sarra, L Meyer, M Bowles, S Wagner-Carena, H Qu, S Golkar, A Bietti, H Bourfoune, P Cornette, K Hirashima, G Krawezik, R Ohana, N Lourie, M McCabe, R Morel, P Mukhopadhyay, M Pettee, K Cho, M Cranmer, S Ho
– Advances in Neural Information Processing Systems 38
(2025)
38,
105685
(doi: 10.52202/085713-3189)
Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme
R Morel, F Ramunno, J Shen, A Bietti, K Cho, M Cranmer, S Golkar, O GUGNIN, G Krawezik, T Marwah, M McCabe, L Meyer, P Mukhopadhyay, R Ohana, L Parker, H Qu, F Rozet, KD Leka, F Lanusse, D Fouhey, S Ho
– Advances in Neural Information Processing Systems
(2025)
38,
74387
(doi: 10.52202/085713-2240)
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,
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 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,
2507
(doi: 10.1093/mnras/stae2689)
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
TMU 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, MJ Smith, C Stone, M Walmsley, JF Wu
(2024)
(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, M Cranmer, S Ho
(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)
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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