Data-Driven Methods for Science and Engineering Seminar
University of Washington, Seattle
Organizers: Joe Bakarji, Jason Bramburger, Henning Lange & Jordan Snyder
Faculty Organizers: Steven L. Brunton, J. Nathan Kutz & Krithika Manohar
Faculty Organizers: Steven L. Brunton, J. Nathan Kutz & Krithika Manohar
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Next Talk
April 2, 2021
Kevin Carlberg
Facebook
Title: TBD
Upcoming Talks and Schedule
April 10, 2021: Dennice Gayme, Johns Hopkins University
May 28, 2021: Eva Kanso, University of Southern California
Previous Talks
March 5, 2021 [ VIEW ]
Prof. Anima Anandkumar
California Institute of Technology
California Institute of Technology
Title: Neural Operator for Parametric PDEs
February 19, 2021 [ VIEW ]
Prof. Beverley McKeon
California Institute of Technology
California Institute of Technology
Title: What's in a mean (what, how and why)? Towards nonlinear models of wall turbulence
February 5, 2021 [ VIEW ]
Tamara Kolda
Sandia National Laboratories
Sandia National Laboratories
Title: Practical Leveraged-Based Sampling for Low-Rank Tensor Decomposition
January 22, 2021 [ VIEW ]
Prof. Zico Kolter
Carnegie Mellon University
Carnegie Mellon University
Title: Incorporating physics and decision making into deep learning via implicit layers
January 8, 2021 [ VIEW ]
Prof. Andrea Bertozzi
UCLA
UCLA
Title: Total variation minimization on graphs for semisupervised and unsupervised machine learning
December 11, 2020 [ VIEW ]
Prof. Cecilia Clementi
FU Berlin
FU Berlin
Title: Designing molecular models by machine learning and experimental data
November 13, 2020 [ VIEW ]
Prof. David Duvenaud
Vector Institute, University of Toronto
Vector Institute, University of Toronto
Title: Handling messy time series with large latent-variable models
October 30, 2020 [ VIEW ]
Prof. Jeff Moehlis
Mechanical Engineering, UC Santa Barbara
Mechanical Engineering, UC Santa Barbara
Title: Learning to control population of neurons
October 16, 2020 [ VIEW ]
Prof. Michael Mahoney
Statistics, Berkeley
Statistics, Berkeley
Title: Dynamical systems and machine learning: combining in a principled way data-driven models and domain-driven models
October 2, 2020 [ VIEW ]
Prof. George Em Karniadakis
Applied Mathematics, Brown University
Applied Mathematics, Brown University
Title: From PINNs to DeepOnets: Approximating functions, functionals, and operators using deep neural networks for diverse applications