Data-Driven Science and Engineering Seminar
University of Washington, Seattle
Faculty Organizers: Steven L. Brunton, J. Nathan Kutz, Krithika Manohar
Faculty Organizers: Steven L. Brunton, J. Nathan Kutz, Krithika Manohar
Upcoming Talks
Details TBA
Pat Langley
Stanford's Center for Design Research
Stanford's Center for Design Research
Previous Talks
April 18, 2024
Steven Rodriguez
U.S. Naval Research Lab
U.S. Naval Research Lab
Enabling Model Reduction of Meshless Nonlocal Methods via Modal Reference Spaces
November 16, 2023
Matthew Juniper
University of Cambridge
University of Cambridge
Adjoint-accelerated Bayesian Inference
October 12, 2023
Adarsh Krishnamurthy
Iowa State University
Iowa State University
Multi-scale Modeling and Simulations using Digital Twins
May 5, 2023
Christine Allen-Blanchette
Princeton University
Princeton University
Learning Dynamics from Images Using Lagrangian/Hamiltonian Structure
Recording not available
April 7, 2023
Samy Wu Fong
Colorado School of Mines
Colorado School of Mines
Using Hamilton-Jacobi PDEs for Optimization
February 3, 2023
David Bortz
University of Colorado Boulder
University of Colorado Boulder
The Surprising Robustness and Computational Efficiency of Weak Form System Identification
January 6, 2023
Tess Smidt
MIT
MIT
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