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"Incorporating dynamical system and control structure into neural networks " by Zico Kolter

Talk Abstract: Neural networks have become a key tool for the modeling and control of dynamical systems. However, typically these networks serve as black boxes, without guarantees of stability, controllability, or other factors that play a foundational role in the analysis of dynamical systems. In this talk, I will discuss several approaches to integrating structure into neural networks to make them better suited to modeling and controlling dynamical systems. This kind of structure includes integrating optimization problems, explicit model predictive control laws, or stability guarantees through Lyapunov projections. Finally, I will also discuss recent work on leveraging advances in neural network verification to formally verify the stability of dynamical systems, offering an insight into how neural networks may also aid in more traditional analysis of such systems. Biography: Zico Kolter is an Associate Professor in the Computer Science Department at Carnegie Mellon University, and also serves as chief scientist of AI research for the Bosch Center for Artificial Intelligence. His work spans the intersection of machine learning and optimization, with a large focus on developing more robust and rigorous methods in deep learning. In addition, he has worked in a number of application areas, highlighted by work on sustainability and smart energy systems. He is a recipient of the DARPA Young Faculty Award, a Sloan Fellowship, and best paper awards at NeurIPS, ICML (honorable mention), AISTATS (test of time), IJCAI, KDD, and PESGM.