L4DC - Learning for Dynamics & Control Conference

Organizers:
external page Ali Jadbabaie (MIT)
John Lygeros (ETH Zurich)
external page George Pappas (Penn)
external page Pablo Parrilo (MIT)
external page Ben Recht (UC Berkley)
external page Davide Scaramuzza (University of Zurich)
external page Claire Tomlin (UC Berkley)
Melanie Zeilinger (ETH Zurich)

Over the next decade, the biggest generator of data is expected to be devices that sense and control the physical world.

The explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning, control theory, and optimization. While control theory has been firmly rooted in the tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking the foundations of our discipline. From a machine learning perspective, one of the main challenges going forward is to go beyond pattern recognition and address problems in data-driven control and optimization of dynamical processes. Our overall goal is to create a new community of people who think rigorously across the disciplines, ask new questions, and develop the foundations of this new scientific area.

Following the success of the inaugural Learning for Dynamics and Control (L4DC) workshop at MIT in 2019, and L4DC 2020 in (virtual) Berkeley, we are very happy to welcome you to Europe and ETH virtual Zurich in 2021.

L4DC 2021
Visit the conference website for more information and free registration!