Plenary Speakers

Koopman Operator Theory Based Machine Learning of Dynamical Systems

Speaker: Dr. Igor Mezić

Date: Oct. 3rd (Tuesday), 2023

Abstract
Many approaches to machine learning have struggled with applications that possess complex process dynamics. In contrast, human intelligence is adapted, and - - arguably - built to deal with complex dynamics. The current theory holds that human brain achieves that by constantly rebuilding a model of the world based on the feedback it receives. I will describe an approach to machine learning of dynamical systems based on Koopman Operator Theory (KOT) that also produces generative, predictive, context-aware models amenable to (feedback) control applications. KOT has deep mathematical roots and I will discuss its basic tenets. I will also present computational methods that enable lean computation. A number of examples will be discussed, including use in fluid dynamics, power grid dynamics, network security, soft robotics, and game dynamics.

Biography
Professor Mezić works in the field of artificial intelligence (AI), dynamical systems, control theory and applications to security, energy efficient design, soft robotics, quantum mechanics and operations in complex systems. He did his Ph. D. in Dynamical Systems at the California Institute of Technology. Dr. Mezic was a postdoctoral researcher at the Mathematics Institute, University of Warwick, UK in 1994-95. From 1995 to 1999 he was a member of College of Engineering at the University of California, Santa Barbara where he is currently a Distinguished Professor. In 2000-2001 he has worked as an Associate Professor at Harvard University in the Division of Engineering and Applied Sciences. He won the Alfred P. Sloan Fellowship, NSF CAREER Award from NSF and the George S. Axelby Outstanding Paper Award from IEEE. He also won the United Technologies Senior Vice President for Science and Technology Special Achievement Prize in 2007. For his work on analysis and control of complex systems, he was named Fellow of the American Physical Society, Fellow of the Society for Industrial and Applied Mathematics and Fellow of the Institute of Electrical and Electronics Engineers. He is the recipient of the 2021 Crawford Prize, awarded once in two years to a researcher in Dynamical Systems Theory. Dr. Mezic is the Director of the Center for Energy Efficient Design and Head of Buildings and Design Solutions Group at the Institute for Energy Efficiency ay the University of California, Santa Barbara. He holds 10 US patents. He founded Aimdyn, Inc. in 2003 and is the co-founder, CTO and Chief Scientist of Mixmode.ai.

Increasingly Autonomous Perception and Decision Systems for Advanced Air Mobility

Speaker: Dr. Ella Atkins

Date: Oct. 4th (Wednesday), 2023

Abstract
Advanced Air Mobility (AAM) including passenger transport and Uncrewed Aircraft Systems (UAS) of all sizes requires autonomy capable of safely managing contingency responses as well as routine flight. This talk will describe pathways from aviation today to a fully autonomous AAM of the future. Research toward comprehensive low-altitude flight environment mapping and urgent landing planning will be summarized with focus on necessary geographical map data curation and processing, real-time perception, and decision algorithms. Dynamic airspace geofencing in support of UAS Traffic Management (UTM) will be defined and compared with traditional fixed airspace corridor solutions. Efforts to effectively present map and decision data to human AAM participants will be summarized.

Biography
Dr. Ella Atkins is Fred D. Durham Professor and Head of the Kevin T. Crofton Aerospace and Ocean Engineering Department at Virginia Tech. She was previously a Professor in the University of Michigan’s Aerospace Engineering and Robotics Departments. Dr. Atkins holds B.S. and M.S. degrees in Aeronautics and Astronautics from MIT and M.S. and Ph.D. degrees in Computer Science and Engineering from the University of Michigan. She is an AIAA Fellow and private pilot. She served on the National Academy’s Aeronautics and Space Engineering Board and has authored over 230 refereed journal and conference papers. Dr. Atkins has pursued research in AI-enabled autonomy and control to support resilience and contingency management in manned and unmanned Aerospace applications. She is Editor-in-Chief of the AIAA Journal of Aerospace Information Systems (JAIS) and a member of the Flight Safety Foundation's Autonomous and Remotely Piloted Aviation Systems Advisory Committee (ARPAC).

Driving Innovation - Real-Time Traffic Prediction for Autonomous Vehicles

Speaker: Dr. Nikolai Smolyanskiy

Date: Oct. 5th (Thursday), 2023

Abstract
AI is revolutionizing every industry —from healthcare and cloud services to manufacturing and robotics. The breakthroughs are nothing short of science fiction. Nowhere is this transformation more significant than in the automotive industry where AVs are set to change the way billions of people move around the world. NVIDIA is at the center of this global effort, combining expertise in AI, software and accelerated computing to build cars with unprecedented levels of safety, security, performance and convenience.

Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving. To this end, we have built PredictionNet, a deep neural network that predicts the motion of all surrounding traffic agents together with the ego-vehicle’s motion. The network can be used to simulate realistic traffic, and it produces competitive results on popular benchmarks. More importantly, it has been used to successfully control a real-world vehicle for hundreds of kilometers, by combining it with a motion planning/control subsystem. The network runs faster than real-time on an embedded GPU, and the system shows good generalization across sensory modalities and locations due to the choice of input representation.

Biography
Dr. Nikolai Smolyanskiy currently serves as the director of deep learning for NVIDIA's automotive group. In this pivotal role, he heads a team composed of accomplished deep-learning specialists and computer vision researchers. Together, they are dedicated to the development and implementation of NVIDIA DRIVE's state-of-the-art software stack, aimed at enabling Level 2+ to Level 4 autonomous driving capabilities.

Within his purview, Smolyanskiy's team tackles a wide array of challenges in the autonomous vehicle (AV) domain, with a particular focus on enhancing LIDAR, RADAR and camera perception, as well as advancing behavior prediction technologies. Notably, Smolyanskiy has spearheaded the successful delivery of a world-class behavior prediction solution, poised to benefit millions of automated vehicles worldwide. Furthermore, Smolyanskiy's expertise extends beyond the automotive sector.

He has also led advanced research efforts in the field of autonomous drones and mobile robotics for the NVIDIA Issac platform. Prior to joining NVIDIA, Smolyanskiy contributed his talents to Microsoft and Microsoft Research, where he made significant contributions to projects involving autonomous drones, computer vision applications for Kinect and Hololens, and machine-learning innovations for intelligent assistants.