2022 TRIPODS Winter School & Workshop on Interplay between Artificial Intelligence and Dynamical Systems

TRIPODS Winter School will be held from January 18th – 21st, 2022. More information will be announced shortly. This event aims to bring together experts in machine learning, dynamical systems, and control theory to share recent research results, fundamental challenges, and new opportunities at the interface between Artificial Intelligence and dynamical systems. The program is composed of a series of keynote talks, tutorials, and hand-on sessions over the course of four days, with opportunities for discussion with peers and leaders in the field.

January 18th – 21st, 2022

Register here

Joshua Agterberg PhD Student Johns Hopkins University	Stephen Boyd Professor Stanford University	Steve Brunton Professor University of Washinton	Maryam Fazel Professor University of Washington	Mahyar Fazlyab Assistant Professor Johns Hopkins University
George Karniadakis Professor Brown University	George Kissas PhD Student University of Pennsylvania	PS Krisnasprasad Professor University of Maryland	Michael Mahoney Associate Professor University of California Berkeley	Hancheng Min PhD Student Johns Hopkins University
Sean Meyn Professor University of Florida	Paris Perdikaris Assistant Professor University of Pennsylvania	Maxim Raginsky Associate Professor University of Illinois at Urbana-Champaign	Jacob Seidman PhD Student University of Pennsylvania	Rose Yu Assistant Professor University of California San Diego

Time	Speaker	Presentation
Tuesday January 18
11:45-12:00 PM	Rene Vidal	Welcome & Introduction
12:00-12:45 PM	Maxim Raginsky	Universal Approximation of Sequence-to-Sequence Transformations by Temporal Convolutional Nets
1:00-1:45 PM	Maryam Fazel	Policy Gradient Descent for Control: Global Optimality via Convex Parameterization
2:00-2:45 PM	Mahyar Fazlyab	Control‐Theoretic Tools in Analysis and Synthesis of Neural Network Driven Systems with Performance Guarantees
3:00-3:20 PM	Hancheng Min	On the convergence and implicit bias of overparametrized linear networks
3:30-4:15 PM	Stephen Boyd	Embedded Convex Optimization for Control
Wednesday January 19
12:00 PM-12:45 PM	Steve Brunton	Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics
1:00 PM-1:45 PM	Rose Yu	Uncertainty Quantification in Learning Spatiotemporal Dynamics
2:00-2:45 PM	George Karniadakis	Approximating functions, functionals and operators with neural networks for diverse applications
Thursday January 20
12:00 PM-12:45 PM	Sean Meyn	Zap Q-learning with Nonlinear Function Approximation
1:00 PM-1:45 PM	Michael Mahoney	Continuous Network Models for Sequential Predictions
2:00-2:45 PM	P.S. Krishnaprasad	Games, Flocks, and Cognition
3:00-3:20 PM	Joshua Agterberg	Entrywise Estimation of Singular Vectors of Low-Rank Matrices with Heteroskedasticity and Dependence
Friday January 21
11:00-1:30 PM	Paris Perdikaris with Georgios Kissas & Jacob Seidman	Supervised learning in function spaces
2:30-5:00 PM	Paris Perdikaris with Georgios Kissas & Jacob Seidman	Supervised learning in function spaces