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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

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)
SRBench plus plus : Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation
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
– IEEE Transactions on Evolutionary Computation
(2024)
29,
1127
(doi: 10.1109/TEVC.2024.3423681)
AstroCLIP: a cross-modal foundation model for galaxies
L Parker, F Lanusse, S Golkar, L Sarra, M Cranmer, A Bietti, M Eickenberg, G Krawezik, M McCabe, R Morel, R Ohana, M Pettee, BR-S Blancard, K Cho, S Ho, TPA Collaboration
– Monthly Notices of the Royal Astronomical Society
(2024)
531,
4990
(doi: 10.1093/mnras/stae1450)
AstroCLIP: A Cross-Modal Foundation Model for Galaxies
L Parker, F Lanusse, S Golkar, L Sarra, M Cranmer, A Bietti, M Eickenberg, G Krawezik, M McCabe, R Ohana, M Pettee, BR-S Blancard, T Tesileanu, K Cho, S Ho
(2024)
(doi: 10.48550/arxiv.2310.03024)
Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task
S Golkar, A Bietti, M Pettee, M Eickenberg, M Cranmer, K Hirashima, G Krawezik, N Lourie, M McCabe, R Morel, R Ohana, LH Parker, BR-S Blancard, K Cho, S Ho
(2024)
(doi: 10.48550/arxiv.2406.02585)
Symbolic Regression on FPGAs for Fast Machine Learning Inference
HF Tsoi, AA Pol, V Loncar, E Govorkova, M Cranmer, S Dasu, P Elmer, P Harris, I Ojalvo, M Pierini
– EPJ Web of Conferences
(2024)
295,
09036
(doi: 10.1051/epjconf/202429509036)
Symbolic Regression on FPGAs for Fast Machine Learning Inference
HF Tsoi, AA Pol, V Loncar, E Govorkova, M Cranmer, S Dasu, P Elmer, P Harris, I Ojalvo, M Pierini
(2024)
(doi: 10.48550/arxiv.2305.04099)
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning.
R Ohana, M McCabe, L Meyer, R Morel, F Agocs, M Beneitez, M Berger, B Burkhart, S Dalziel, D Fielding, D Fortunato, J Goldberg, K Hirashima, Y-F Jiang, R Kerswell, S Maddu, J Miller, P Mukhopadhyay, S Nixon, J Shen, R Watteaux, B Blancard, F Rozet, L Parker, M Cranmer, S Ho
– Advances in Neural Information Processing Systems 37
(2024)
37,
44989
(doi: 10.52202/079017-1430)
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)
TheWell: a Large-Scale Collection of Diverse Physics Simulations forMachine Learning
R Ohana, M McCabe, L Meyer, R Morel, FJ Agocs, M Beneitez, M Berger, B Burkhart, 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, B Regaldo-Saint Blancard, F Rozet, LH Parker, M Cranmer, S Ho
– ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 37 (NEURIPS 2024)
(2024)
(link to publication)
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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