
 
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
Faculty Organizers: Steven L. Brunton, J. Nathan Kutz & Krithika Manohar
 
Mailing List: [ SIGN UP ]
 
Upcoming Talks
 

February 3, 2023
 
David Bortz
University of Colorado Boulder
University of Colorado Boulder
 
Title: The Surprising Robustness and Computational Efficiency of Weak Form System Identification
 
 
Previous Talks
 

January 6, 2023
 
Tess Smidt
MIT
MIT
 
Title: Double Dipping: Problems and solutions, with applications to single-cell RNA-sequencing data
 
 

December 2, 2022
 
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
 
 

 
Jorn Dunkel
MIT
MIT
 
Title: Symmetry-informed model inference for active matter
 
 

 
Andrew Stuart
California Institute of Technology
California Institute of Technology
 
Title: Supervised Learning for Operators
 
 

April 1, 2022 [VIEW ]
 
Rich Baraniuk
Rice University
Rice University
 
Title: Deep Network Spline Geometry
 
 

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 28, 2021 [ VIEW ]
 
Eva Kanso
University of Southern California
University of Southern California
 
Title: One Fish, Two Fish
 
 

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