Data-Driven Science and Engineering Seminar
University of Washington, Seattle
Organizers: Joe Bakarji, Ryan Raut & Zack Nicolaou
Faculty Organizers: Steven L. Brunton, J. Nathan Kutz & Krithika Manohar
Organizers: Joe Bakarji, Ryan Raut & Zack Nicolaou
Faculty Organizers: Steven L. Brunton, J. Nathan Kutz & Krithika Manohar
Mailing List: SIGN UP
Upcoming Talks
Seminars will resume in fall 2023
Previous Talks
May 5, 2023 [recording not available]
Christine Allen-Blanchette
Princeton University
Princeton University
Title: Learning Dynamics from Images Using Lagrangian/Hamiltonian Structure
Samy Wu Fong
Colorado School of Mines
Colorado School of Mines
Title: Using Hamilton-Jacobi PDEs for Optimization
David Bortz
University of Colorado Boulder
University of Colorado Boulder
Title: The Surprising Robustness and Computational Efficiency of Weak Form System Identification
Tess Smidt
MIT
MIT
Title: Double Dipping: Problems and solutions, with applications to single-cell RNA-sequencing data
Daniela Witten
University of Washington
University of Washington
Title: Double Dipping: Problems and solutions, with applications to single-cell RNA-sequencing data
Melanie Weber
Harvard University
Harvard University
Title: Exploiting Geometric Structure in Machine Learning and Optimization
John Wright
Columbia University
Columbia University
Title: Deep Networks and the Multiple Manifold Problem
Andrew Stuart
California Institute of Technology
California Institute of Technology
Title: Supervised Learning for Operators
March 4, 2022 [VIEW ]
Liliana Borcea
University of Michigan
University of Michigan
Title: Waveform inversion via reduced order modeling
February 4, 2022 [VIEW ]
Laure Zanna
Courant Institute for Mathematical Sciences, NYU
Courant Institute for Mathematical Sciences, NYU
Title: Data-driven turbulence closures for ocean and climate models: advances and challenges
December 3, 2021 [ VIEW ]
Joan Bruna Estrach
Courant Institute of Mathematical Sciences, NYU
Courant Institute of Mathematical Sciences, NYU
Title: Prospects and Challenges of Machine Learning in the Physical World
November 19, 2021 [ VIEW ]
Benjamin Peherstorfer
Courant Institute of Mathematical Sciences, NYU
Courant Institute of Mathematical Sciences, NYU
Title: Physics-based machine learning for quickly simulating transport-dominated physical phenomena
November 12, 2021 [ VIEW ]
Jane Bae
California Institute of Technology
California Institute of Technology
Title: Wall-models of turbulent flows via scientific multi-agent reinforcement learning
November 5, 2021 [ VIEW ]
Rose Yu
University of California, San Diego
University of California, San Diego
Title: Incorporating symmetry for learning spatiotemporal dynamics
May 14, 2021 [ VIEW ]
Nicholas Zabaras
Notre Dame
Notre Dame
Title: Physics Informed Learning for Multiscale Dynamical Systems
April 30, 2021
Rachel Ward
University of Texas, Austin
University of Texas, Austin
Title: Generalization bounds for sparse random feature expansions
April 16, 2021
Dennice Gayme
Johns Hopkins University
Johns Hopkins University
Title: A new paradigm in wind farm modeling and control for power grid support
April 9, 2021 [ VIEW ]
Kevin Carlberg
University of Washington
University of Washington
Title: AI for Computational Physics: Toward real-time high-fidelity simulation
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