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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

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
JW Park, S Birrer, M Ueland, M Cranmer, A Agnello, S Wagner-Carena, PJ Marshall, A Roodman, TLDES Collaboration
(2022)
A Neural Network Subgrid Model of the Early Stages of Planet Formation
T Pfeil, M Cranmer, S Ho, PJ Armitage, T Birnstiel, H Klahr
(2022)
$\texttt{Mangrove}$: Learning Galaxy Properties from Merger Trees
CK Jespersen, M Cranmer, P Melchior, S Ho, RS Somerville, A Gabrielpillai
(2022)
HIFlow: Generating Diverse Hi Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows
Hassan, F Villaescusa-Navarro, B Wandelt, DN Spergel, D Anglés-Alcázar, S Genel, M Cranmer, GL Bryan, R Davé, RS Somerville, M Eickenberg, D Narayanan, S Ho, S Andrianomena
– The Astrophysical Journal
(2022)
937,
83
GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
A Ghosh, CM Urry, A Rau, L Perreault-Levasseur, M Cranmer, K Schawinski, D Stark, C Tian, R Ofman, TT Ananna, C Auge, N Cappelluti, DB Sanders, E Treister
– The Astrophysical Journal
(2022)
935,
138
HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows
S Hassan, F Villaescusa-Navarro, B Wandelt, DN Spergel, D Anglés-Alcázar, S Genel, M Cranmer, GL Bryan, R Davé, RS Somerville, M Eickenberg, D Narayanan, S Ho, S Andrianomena
(2022)
Automated discovery of interpretable gravitational-wave population models
KWK Wong, M Cranmer
(2022)
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
A Ghosh, CM Urry, A Rau, L Perreault-Levasseur, M Cranmer, K Schawinski, D Stark, C Tian, R Ofman, TT Ananna, C Auge, N Cappelluti, DB Sanders, E Treister
(2022)
TNT: Vision Transformer for Turbulence Simulations
Y Dang, Z Hu, M Cranmer, M Eickenberg, S Ho
(2022)
Predicting the thermal Sunyaev–Zel’dovich field using modular and equivariant set-based neural networks
L Thiele, M Cranmer, W Coulton, S Ho, DN Spergel
– Machine Learning: Science and Technology
(2022)
3,
035002
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Research Group

Relativity and Gravitation

Room

B2.17