DDLC Seminar: Peter Seiler (Michigan)

Description

Data Driven Learning and Control seminar series is organized by the Information and Decision Science Lab at Cornell University and aims to explore the latest advancements and interdisciplinary approaches to data-driven learning and control systems.

Watch on YouTube Live
 

Bio: Peter Seiler works in the area of robust control theory which focuses on the impact of model uncertainty on systems design. He is a contributor to the Robust Control Toolbox in Matlab and is currently developing theoretical and numerical algorithms to assess the robustness of systems on finite time horizons. He is also investigating the use of robust control techniques to better understand optimization algorithms and model-free reinforcement learning methods.

Seiler joined Michigan in 2020 from the University of Minnesota, where he had been working on advanced control techniques for wind turbines, fault-detection methods for safety-critical systems and robust control of disk drives.

From 2004-2008, he worked at the Honeywell Research Labs on various aerospace and automotive applications including the redundancy management system for the Boeing 787, sensor fusion algorithms for automotive active safety systems and re-entry flight control laws for NASA’s Orion vehicle. Seiler joined the University of Minnesota in 2008, where he worked on advanced control techniques for wind turbines, fault-detection methods for safety-critical systems and robust control of disk drives. His research is grounded in robust control theory which focuses on the impact of model uncertainty on systems design.

Seiler earned his Ph.D. in mechanical engineering from the University of California, Berkeley and B.S. degrees in mechanical engineering and mathematics from the University of Illinois at Urbana-Champaign.