AI Institute Data-Driven Seminars
Upcoming
To Be Announced
Previous
November 1, 2024
Ana Larrañaga Janeiro
Postdoctoral Researcher at CINTECX (Universidade de Vigo)
Postdoctoral Researcher at CINTECX (Universidade de Vigo)
Learning the shape: streamlining data needs in 2D irregular contour parameterization
October 25, 2024
Andrei Klishin
Postdoctoral Scholar, AI Institute in Dynamic Systems
Department of Mechanical Engineering at University of Washington
Postdoctoral Scholar, AI Institute in Dynamic Systems
Department of Mechanical Engineering at University of Washington
Statistical mechanics lessons for data-driven methods
October 18, 2024
Nicolò Botteghi
Postdoctoral Researcher at the Mathematics of Imaging and AI group in the Department of Applied Mathematics at the University of Twente
Postdoctoral Researcher at the Mathematics of Imaging and AI group in the Department of Applied Mathematics at the University of Twente
Data Efficient, Robust, and Interpretable Deep Reinforcement Learning for Robotics and Dynamical Systems
July 18, 2024
Alan Kaptanoglu
Assistant Professor of Mathematics at the Courant Institute at NYU
Assistant Professor of Mathematics at the Courant Institute at NYU
Sparse regression in inverse magnetostatics
June 13, 2024
Sabera Talukder
National Science Foundation Graduate Fellow, Chen Graduate Fellow
PhD candidate at Caltech
Chair, COSYNE Organizing Committee
National Science Foundation Graduate Fellow, Chen Graduate Fellow
PhD candidate at Caltech
Chair, COSYNE Organizing Committee
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
Recording not available
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
Recording not available
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
December 2, 2022
Daniela Witten
University of Washington
University of Washington
Double Dipping: Problems and solutions, with applications to single-cell RNA-sequencing data
November 4, 2022
Melanie Weber
Harvard University
Harvard University
Exploiting Geometric Structure in Machine Learning and Optimization
October 7, 2022
John Wright
Columbia University
Columbia University
Deep Networks and the Multiple Manifold Problem
May 6, 2022
Andrew Stuart
California Institute of Technology
California Institute of Technology
Supervised Learning for Operators
March 4, 2022
Liliana Borcea
University of Michigan
University of Michigan
Waveform inversion via reduced order modeling
February 4, 2022
Laure Zanna
Courant Institute for Mathematical Sciences, NYU
Courant Institute for Mathematical Sciences, NYU
Data-driven turbulence closures for ocean and climate models: advances and challenges
December 3, 2021
Joan Bruna Estrach
Courant Institute of Mathematical Sciences, NYU
Courant Institute of Mathematical Sciences, NYU
Prospects and Challenges of Machine Learning in the Physical World
November 19, 2021
Benjamin Peherstorfer
Courant Institute of Mathematical Sciences, NYU
Courant Institute of Mathematical Sciences, NYU
Physics-based machine learning for quickly simulating transport-dominated physical phenomena
November 12, 2021
Jane Bae
California Institute of Technology
California Institute of Technology
Wall-models of turbulent flows via scientific multi-agent reinforcement learning
November 5, 2021
Rose Yu
University of California, San Diego
University of California, San Diego
Incorporating symmetry for learning spatiotemporal dynamics
May 14, 2021 [ VIEW ]
Nicholas Zabaras
Notre Dame
Notre Dame
Physics Informed Learning for Multiscale Dynamical Systems
April 30, 2021
Rachel Ward
University of Texas, Austin
University of Texas, Austin
Generalization bounds for sparse random feature expansions
April 16, 2021
Dennice Gayme
Johns Hopkins University
Johns Hopkins University
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
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
Neural Operator for Parametric PDEs
February 19, 2021 [ VIEW ]
Prof. Beverley McKeon
California Institute of Technology
California Institute of Technology
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
Practical Leveraged-Based Sampling for Low-Rank Tensor Decomposition
January 22, 2021 [ VIEW ]
Prof. Zico Kolter
Carnegie Mellon University
Carnegie Mellon University
Incorporating physics and decision making into deep learning via implicit layers
January 8, 2021 [ VIEW ]
Prof. Andrea Bertozzi
UCLA
UCLA
Total variation minimization on graphs for semisupervised and unsupervised machine learning
December 11, 2020 [ VIEW ]
Prof. Cecilia Clementi
FU Berlin
FU Berlin
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
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
Learning to control population of neurons
October 16, 2020 [ VIEW ]
Prof. Michael Mahoney
Statistics, Berkeley
Statistics, Berkeley
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
From PINNs to DeepOnets: Approximating functions, functionals, and operators using deep neural networks for diverse applications
