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Department of Applied Mathematics and Theoretical Physics

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

A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
VA Villar, M Cranmer, E Berger, G Contardo, S Ho, G Hosseinzadeh, JY-Y Lin
(2021)
A Bayesian neural network predicts the dissolution of compact planetary systems
M Cranmer, D Tamayo, H Rein, P Battaglia, S Hadden, PJ Armitage, S Ho, DN Spergel
(2021)
Unsupervised Resource Allocation with Graph Neural Networks
M Cranmer, P Melchior, B Nord
– Proceedings of Machine Learning Research
(2021)
148,
272
Discovering Symbolic Models from Deep Learning with Inductive Biases
M Cranmer, A Sanchez-Gonzalez, P Battaglia, R Xu, K Cranmer, D Spergel, S Ho
(2020)
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
VA Villar, M Cranmer, G Contardo, S Ho, JY-Y Lin
(2020)
Lagrangian Neural Networks
M Cranmer, S Greydanus, S Hoyer, P Battaglia, D Spergel, S Ho
(2020)
Predicting the long-term stability of compact multiplanet systems
D Tamayo, M Cranmer, S Hadden, H Rein, P Battaglia, A Obertas, PJ Armitage, S Ho, DN Spergel, C Gilbertson, N Hussain, A Silburt, D Jontof-Hutter, K Menou
– Proceedings of the National Academy of Sciences of the United States of America
(2020)
117,
18194
Predicting the long-term stability of compact multiplanet systems
D Tamayo, M Cranmer, S Hadden, H Rein, P Battaglia, A Obertas, PJ Armitage, S Ho, D Spergel, C Gilbertson, N Hussain, A Silburt, D Jontof-Hutter, K Menou
(2020)
Discovering symbolic models from deep learning with inductive biases
M Cranmer, A Sanchez-Gonzalez, P Battaglia, R Xu, K Cranmer, D Spergel, S Ho
– Advances in Neural Information Processing Systems
(2020)
2020-December,
Learning Symbolic Physics with Graph Networks
MD Cranmer, R Xu, P Battaglia, S Ho
(2019)
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Research Group

Relativity and Gravitation

Room

B2.17