# Webinar Speakers

*All webinars start at 1:00 pm PT and 4:00 pm ET.*

## 2022

April 11, 2022 |
Houman Owhadi |

April 4, 2022 |
Laure Zanna |

March 14, 2022 |
Mengjia Xu |

March 7, 2022 |
Priya Panda |

February 28, 2022 |
Lai-Yung (Ruby) Leung |

February 21, 2022 |
Kara Peterson |

February 14, 2022 | Mamikon Gulian Sandia National Laboratories "Connections Between Nonlocal Operators: From Vector Calculus Identities to a Fractional Helmholtz Decomposition" |

January 31, 2022 | Jonas Landman QC Ware & The University of Edinburgh "Quantum Algorithms and Machine Learning" |

## 2021

December 6, 2021 | Grace Gu University of California, Berkeley "Generative Design and Additive Manufacturing of Three-Dimensional Architected Metamaterials" |

November 15, 2021 |
Brad Aimone |

August 30, 2021 |
Remi Dingreville |

August 16, 2021 |
Youngsoo Choi |

July 12, 2021 |
Marta D'Elia |

June 14, 2021 |
Mark Ainsworth |

June 7, 2021 |
Nat Trask |

May 10, 2021 | Paul Atzberger University of California, Santa Barbara "Machine Learning for Investigating Dynamics of Physical Systems" |

May 4, 2021 | Greg Valiant Stanford University "Calibration, Mis-Specification, and Selective Learning" |

April 26, 2021 |
Costis Daskalakis |

April 5, 2021 |
Mihai Anitescu |

March 22, 2021 |
Samuel Lanthaler |

February 22, 2021 |
Henry Abarbanel |

January 18, 2021 |
Andrew Stuart |

## 2020

December 14, 2020 | Kevin Carlberg University of Washington "Nonlinear model reduction: using machine learning to enable rapid simulation of extreme-scale physics models" |

November 23, 2020 |
Kenneth Golden |

November 16, 2020 |
Patrick Kidger |

September 28, 2020 | Dirk Hartmann Senior Principal Scientist, Siemens "Mathematics a key enabler for Digital Twins" |

September 21, 2020 | Eric Vander-Eijnden New York University "Machine learning and PDEs" |

August 17, 2020 | Mamikon Gulian Sandia National Laboratories "A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges" |

August 3, 2020 | Chinmay Hedge New York University "Untrained Neural Priors: Theory and Applications to PDEs" |

July 13, 2020 | Bethany Lusch Argonne National Laboratory "Scientific Machine Learning at the Argonne Leadership Computing Facility" |

July 6, 2020 | Professor Christoph Schwab ETH-Zurich University "Numerical Analysis of Deep Neural Networks for PDEs" |

June 29, 2020 | Pavel Bochev Sandia National Laboratories "Data-driven models for photocurrent effects in semiconductor devices" |

June 22, 2020 | Eric Cyr Sandia National Laboratories "A Layer-Parallel Approach for Training Deep Neural Networks" |

June 8, 2020 | Lars Ruthotto Emory University "Machine Learning meets Optimal Transport: Old solutions for new problems and vice versa" |

June 1, 2020 | Wei Zhu Duke University "Applied differential geometry and harmonic analysis in deep learning regularization" |

May 18, 2020 | Aidan Thompson Sandia National Laboratories "Predictive Atomistic Simulations of Materials using SNAP Data-Driven Potentials" |

May 11, 2020 | Hrushikesh Mhaskar Claremont Graduate University "Learning with an asymptotically optimal number of samples" |

May 4, 2020 | Yeonjong Shin Brown University "On the Convergence and Generalization of Physics Informed Neural Networks" |

April 27, 2020 | Eric Darve and Kailai Xu Stanford University "Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning" |

April 27, 2020 | Qizhi He Pacific Northwest National Laboratory "Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport" |

April 13, 2020 | Nat Trask Sandia National Laboratories "Physics informed graph neural networks: a unification of PINNs with mimetic PDE discretizations" |

February 10, 2020 | Celia Reina University of Pennsylvania "Harnessing Fluctuations to Discover Dissipative Evoluation Equations" |