Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high‐resolution simulations T Schneider, S Lan, A Stuart, J Teixeira Geophysical Research Letters 44 (24), 12,396-12,417, 2017 | 366 | 2017 |

Geometric MCMC for infinite-dimensional inverse problems A Beskos, M Girolami, S Lan, PE Farrell, AM Stuart Journal of Computational Physics 335, 327-351, 2017 | 163 | 2017 |

Calibrate, emulate, sample E Cleary, A Garbuno-Inigo, S Lan, T Schneider, AM Stuart Journal of Computational Physics 424, 109716, 2021 | 101 | 2021 |

Split hamiltonian monte carlo B Shahbaba, S Lan, WO Johnson, RM Neal Statistics and Computing 24, 339-349, 2014 | 97 | 2014 |

Wormhole hamiltonian monte carlo S Lan, J Streets, B Shahbaba Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 86 | 2014 |

Spherical Hamiltonian Monte Carlo for constrained target distributions S Lan, B Zhou, B Shahbaba International Conference on Machine Learning, 629-637, 2014 | 73 | 2014 |

phylodyn: an R package for phylodynamic simulation and inference MD Karcher, JA Palacios, S Lan, VN Minin Molecular ecology resources 17 (1), 96-100, 2017 | 59 | 2017 |

Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian inverse problems S Lan, T Bui-Thanh, M Christie, M Girolami Journal of Computational Physics 308, 81-101, 2016 | 59 | 2016 |

Markov chain monte carlo from lagrangian dynamics S Lan, V Stathopoulos, B Shahbaba, M Girolami Journal of Computational and Graphical Statistics 24 (2), 357-378, 2015 | 52 | 2015 |

An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics S Lan, JA Palacios, M Karcher, VN Minin, B Shahbaba Bioinformatics 31 (20), 3282-3289, 2015 | 40 | 2015 |

Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation A Holbrook, S Lan, A Vandenberg-Rodes, B Shahbaba Journal of statistical computation and simulation 88 (5), 982-1002, 2018 | 35 | 2018 |

Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry T House, A Ford, S Lan, S Bilson, E Buckingham-Jeffery, M Girolami Journal of the Royal Society Interface 13 (121), 20160279, 2016 | 23 | 2016 |

Sampling constrained probability distributions using spherical augmentation S Lan, B Shahbaba Algorithmic Advances in Riemannian Geometry and Applications: For Machine …, 2016 | 21 | 2016 |

Scaling up bayesian uncertainty quantification for inverse problems using deep neural networks S Lan, S Li, B Shahbaba SIAM/ASA Journal on Uncertainty Quantification 10 (4), 1684-1713, 2022 | 17 | 2022 |

Adaptive dimension reduction to accelerate infinite-dimensional geometric Markov Chain Monte Carlo S Lan Journal of Computational Physics 392, 71-95, 2019 | 17 | 2019 |

Lagrangian Dynamical Monte Carlo S Lan, V Stathopoulos, B Shahbaba, M Girolami arXiv preprint arXiv:1211.3759, 2012 | 17 | 2012 |

A semiparametric Bayesian model for detecting synchrony among multiple neurons B Shahbaba, B Zhou, S Lan, H Ombao, D Moorman, S Behseta Neural computation 26 (9), 2025-2051, 2014 | 11 | 2014 |

Flexible Bayesian dynamic modeling of correlation and covariance matrices S Lan, A Holbrook, GA Elias, NJ Fortin, H Ombao, B Shahbaba Bayesian analysis 15 (4), 1199, 2020 | 10 | 2020 |

Nonparametric fisher geometry with application to density estimation A Holbrook, S Lan, J Streets, B Shahbaba Conference on Uncertainty in Artificial Intelligence, 101-110, 2020 | 10 | 2020 |

Deep markov chain monte carlo B Shahbaba, LM Lomeli, T Chen, S Lan arXiv preprint arXiv:1910.05692, 2019 | 9 | 2019 |