Tutorial Sessions

Tutorial Sessions by Panagiotis Tsiotras and Emmanuel Abbe

will be presented on Tuesday, September 27, 2016* 

Coordinated Science Laboratory Auditorium, Room B02

Registration Fee Per Session
$40 advanced registration fee (before September 18)
$50 regular registration fee (September 19-27)

SCHEDULE

Registration and Breakfast: 8:30-9:30 am in the lower level lobby of Coordinated Science Laboratory

Morning Tutorial with Panagiotis Tsiotras
Session begins: 9:30am-11:30am
Lunch break: 11:30am-12:30pm (Lunch will not be provided on site)
Session resumes: 12:30pm-1:30pm

Afternoon Tutorial with Emmanuel Abbe
Session begins: 2:00pm-3:30pm
Afternoon break: 3:30pm-4:00pm (Snacks and water will be provided)
Session resumes: 4:00pm-5:30pm


ptsiotras

Panagiotis Tsiotras, Georgia Institute of Technology will start his tutorial presentation at 9:30am-1:30pm (includes a break for lunch from 11:30am-12:30pm)

Title: Recent Advances on Computing Optimal Trajectories: From Continuous to Discrete and from Deterministic to Stochastic and Back

Abstract: Since its inception in the late 1950’s optimal control theory has been the cornerstone of many major technological developments in aerospace engineering and related fields. Despite six decades of continuous advances in this area, real-time optimal trajectory generation remains the elusive “holy grail’’ of control theorists. The recent emergence of autonomous robotic systems has brought along with it challenges that are beyond the capabilities of traditional trajectory optimizers. In this talk we will give an overview of some recent results for computing optimal trajectories for autonomous systems that leverage multi-scale methods in order to compute efficiently and robustly optimal trajectories for highly nonlinear, realistic systems. Nonetheless, the usefulness of these techniques diminishes quickly when one wants to plan trajectories in high-dimensional state/configuration spaces, especially in the presence of several state constraints and/or obstacles. To remedy some of these hindrances, recently there has been an increased focus on the use of probabilistic sampling-based techniques. We will present a new class of sampling-based methods that lead to “self-adapted” multi-resolution graph abstractions. We expedite convergence using ideas from approximate dynamic programming, thus bridging the gap between these recent methods and the more traditional methods based on optimal control. Finally, we discuss a new class of trajectory optimization methods for stochastic systems that are not gradient-based but are rather based on ideas from statistical mechanics and the use of path-integrals.

Biography: Dr. Panagiotis Tsiotras is the Dean’s Professor at the School of Aerospace Engineering at Georgia Tech. At Georgia Tech, he is the Director of the Dynamics and Control Systems Laboratory and the Associate Director for Research for the Institute for Robotics and Intelligent Machines (IRIM). His current research interests are in optimal and nonlinear control and their connections with AI, with applications to vehicle autonomy. He received his PhD degree in Aeronautics and Astronautics from Purdue in 1993. He also holds degrees in Mechanical Engineering and Mathematics. He is a recipient of the NSF CAREER award, the Sigma Xi Society Excellence in Research award, and the Purdue University 2014 School of Aeronautics and Astronautics’ Outstanding Aerospace Engineer (OAE) award (highest honor bestowed on that school’s alumni). He is currently the Chief Editor of the Frontiers of Robotics and AI in the area of Space Robotics. Previously, he served at the Editorial Boards of the AIAA Journal of Guidance, Control, and Dynamics, the IEEE Transactions of Automatic Control, the IEEE Control Systems Magazine, and the Journal of Dynamical and Control Systems. He is a Fellow of AIAA and a Senior Member of the IEEE.


profile_pic_abbe

Emmanuel Abbe, Princeton University will start his tutorial presentation at 2:00pm-5:30pm (includes a 30 minute break from 3:30-4:00pm)

Title: Community Detection and Inference in Graphs

Abstract: This tutorial covers recent developments on community detection for the stochastic block model (SBM). The first part covers basic random graphs tools, the second part covers basic results for the SBM with two communities, and the third part discusses recent results for the general stochastic block model, with connections to other inference on graph problems. Emphasis is put on the phase transition phenomena and on the information-theoretic vs. computational tradeoff.

Biography: Emmanuel Abbe received his Ph.D. degree from the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, and his M.S. degree from the Mathematics Department at the Ecole Polytechnique Fédérale de Lausanne. He is currently an assistant professor at Prineton University, jointly in the Program for Applied and Computational Mathematics and the Department of Electrical Engineering. He is the recipient of the CVCI Prize in Mathematics at EPFL, the Foundation Latsis International Prize, the Bell Labs Prize, the NSF CAREER Award and the Google Faculty Research Award.

*Please note that lodging will not be available on Monday, September 26 at the Allerton Park and Retreat Center. If you plan to attending these tutorials, please pursue other lodging options at the 2016 Recommended Hotels section.

Metered parking is available. Please pay via cell phone or plan accordingly. Change will not be provided by organizers.