Call for Participation

Update: The organizers became aware of the ShutDownSTEM movement (www.shutdownstem.com), which is supporting a one day strike from academic life on Wednesday June 10. The goal of the strike is to emphasize the need to transition to a real commitment to removing anti-black racism in academic institutions and STEM specifically. In solidarity, we have decided to move the Wednesday program to Friday.

Talks & Poster session will be broadcast live on June 10-11 June 11-12, no registration necessary.

A link to access the talks via YouTube Live Streaming 15:30-20:00 UTC will be published here by June 11

A link to participate in the Poster Sessions in OpenReview will be published here by June 11

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

This 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 tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking of 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 that think rigorously across the disciplines, asks new questions, and develops the foundations of this new scientific area.

Following the success of the inaugural L4DC workshop 2019 at MIT, the 2nd Annual Conference on Learning for Dynamics and Control (L4DC 2020) will take place on June 10-11, June 11-12, 2020, online.

The program will consist of invited talks, talks from contributed papers, and poster sessions. This year, 135 papers were received and reviewed by the L4DC program committee, 14 have been accepted for presentation and 85 for posters. Invited speakers include:

Professor Chelsea Finn, CS and EE, Stanford

Leslie Pack Kaelbling, EECS, MIT

John Lygeros, Automatic Control Laboratory, ETH Zurich

Karen Willcox, Oden Institute for Computational Engineering and Sciences, UT Austin

Catherine Wolfram, Haas School of Business, UC Berkeley