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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

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.
M Cranmer, S Dalziel, R Kerswell, M Beneitez
– NeurIPS
(2024)
(doi: 10.17863/CAM.113689)
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)
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
M Cranmer, K Rich R., D Stuart B., B Miguel
– Advances in Neural Information Processing Systems
(2024)
abs/2412.00568,
Symbolic Regression with a Learned Concept Library
A Grayeli, A Sehgal, O Costilla-Reyes, M Cranmer, S Chaudhuri
– Advances in Neural Information Processing Systems
(2024)
37,
(link to publication)
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data
E Angeloudi, J Audenaert, M Bowles, BM Boyd, D Chemaly, B Cherinka, I Ciuca, M Cranmer, A Doh, M Grayling, EE Hayes, T Hehir, S Ho, M Huertas-Company, KG Iyer, M Jablonska, F Lanusse, HW Leung, K Mandel, JR Martinez-Galarza, P Melchior, L Meyer, LH Parker, H Qu, J Shen, MJ Smith, C Stone, M Walmsley, JF Wu
– ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 37 (NEURIPS 2024)
(2024)
37,
(link to publication)
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)
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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