- "Advanced Deep Learning Methods for Autonomous Mobility"
- Prof. Joongheon Kim
- Korea University, Korea
This presentation is for introducing and discussing deep learning techniques for autonomous aerial mobility. First of all, this presentation introduces various types of multi-agent deep reinforcement learning algorithms for autonomous mobility platforms such as unmanned aerial vehicles, urban air mobility (UAM), autonomous distributed robots, and etc. Furthermore, this presentation explains deep learning solutions for Myerson-based second price auction (SPA) approximations to distributed and truthful resource allocations in autonomous mobility platforms. Lastly, this presentation will discuss future research directions for deep learning techniques for autonomous mobility.
Dr. Joongheon Kim has been with Korea University, Seoul, Korea, since 2019, and he is currently an associate professor at the School of Electrical Engineering. He received the B.S. and M.S. degrees in Computer Science and Engineering from Korea University, Seoul, Korea, in 2004 and 2006, respectively; and the Ph.D. degree in Computer Science from the University of Southern California (USC), Los Angeles, CA, USA, in 2014. Before joining Korea University, he was with LG Electronics (Seoul, Korea, 2006--2009), InterDigital (San Diego, CA, USA, 2012), Intel Corporation (Santa Clara in Silicon Valley, CA, USA, 2013--2016), and Chung-Ang University (Seoul, Korea, 2016--2019). He is a senior member of the IEEE, and serves as an associate editor for IEEE Transactions on Vehicular Technology. He was a recipient of Annenberg Graduate Fellowship with his Ph.D. admission from USC (2009), Intel Corporation Next Generation and Standards (NGS) Division Recognition Award (2015), IEEE Vehicular Technology Society (VTS) Seoul Chapter Award (3 times in 2019 and 2021), IEEE Systems Journal Best Paper Award (2020), IEEE ICOIN Best Paper Award (2021), and Haedong Paper Award by KICS (2021).
- "Reliable Real-Time Distributed AI for Mobile Autonomous Systems"
- Prof. Marco Levorato (with Yoshitomo Matsubara)
- University of California, USA
The autonomous operations of Mobile Autonomous Systems (MAS) require the execution of continuous streams of heavy-duty mission-critical computing tasks. These tasks often take the form of complex Deep Neural Network (DNN) models applied to information-rich signals such as image and lidar data. Clearly, such strenuous effort may exceed the capabilities and resources (e.g., computing power and energy) of most UAVs. The research community mostly relied on two distinct approaches to address this issue: model simplification and edge computing. The former may lead to performance degradation, while the performance of the latter suffer from the erratic behavior of wireless channels.
In this tutorial, we will introduce and illustrate some of the most recent trends in making distributed computing and edge computing for MAS applications reliable. First, we will discuss machine learning-empowered solutions to control real-time data analysis and inference in complex systems. Our discussion will be based on our open source tool that enables real-time decision making for computing in this class of difficult systems. Then, we will discuss Split Deep Neural Networks (DNN) - also known as Split Computing, a recent class of solutions where DNNs’ architectures and training are modified to facilitate their efficient distributed execution. In the tutorial, we will illustrate existing technical approaches and tools in relation to split computing, including model designs and training methods such as supervised compression.
He is a Ph.D. Candidate in Computer Science at the University of California, Irvine (UCI), working on computer vision and NLP projects with Profs. Marco Levorato, Sameer Singh, and Stephan Mandt. Before UCI, he obtained his Master’s and Bachelor’s degrees from the University of Hyogo and the National Institute of Technology, Akashi College, respectively. His Master’s and Bachelor’s theses’ topics were behavioral biometrics, such as keystroke dynamics, and flick authentication. He also has work experience at industry such as Amazon.com Services, Inc., Slice Technologies, Yahoo Japan Corporation, and Recruit Holdings Co., Ltd. for research projects in various domains: NLP, information retrieval, computer vision, and recommender systems.
Marco Levorato joined the Computer Science department at UCIrvine in August 2013. He completed the PhD in Electrical Engineering at the University of Padova, Italy, in 2009. He obtained the B.S. and M.S. in Electrical Engineering summa cum laude at the University of Ferrara, Italy in 2005 and 2003, respectively. Between 2010 and 2012, He was a post-doctoral researcher with a joint affiliation at Stanford and the University of Southern California. His research interests are focused on distributed computing over unreliable wireless systems, especially for autonomous vehicles. His work received the best paper award at IEEE GLOBECOM (2012). In 2016 and 2019, he received the UC Hellman Foundation Award and the Dean mid-career research award, respectively.
- "Benefits and Risks of Sensing for Emerging Internet-of-Things Applications"
- Jun Han
- Yonsei University, Korea
With the emergence of the Internet-of-Things (IoT) and Cyber-Physical Systems (CPS), we are witnessing a wealth of exciting applications that enable computational devices to interact with the physical world via an overwhelming number of sensors and actuators. However, such interactions pose new challenges to traditional approaches of security and privacy. In this talk, I will present how I utilize sensor data to provide security and privacy protections for IoT/CPS scenarios, and further introduce novel security threats arising from similar sensor data. Specifically, I will highlight some of our recent projects that leverage sensor data for attack and defense in various IoT applications. I will also introduce my future research directions such as identifying and defending against unforeseen security challenges from newer application domains including smart homes, buildings, and vehicles.
Jun Han is an Assistant Professor at Yonsei University with an appointment in the School of Electrical and Electronic Engineering. He founded and directs the Cyber-Physical Systems and Security (CyPhy) Lab at Yonsei. Prior to joining Yonsei, he was at the National University of Singapore with an appointment in the Department of Computer Science, School of Computing. His research interests lie at the intersection of sensing systems and security, and focuses on utilizing contextual information for security applications in the Internet-of-Things and Cyber-Physical Systems. He publishes at top-tier venues across various research communities spanning security, sensing systems, and mobile computing (including S&P/Oakland, Usenix Security, CCS, SenSys IPSN). He received his Ph.D. from the Electrical and Computer Engineering Department at Carnegie Mellon University as a member of Mobile, Embedded, and Wireless (MEWS) Group. He received his M.S. and B.S. degrees in Electrical and Computer Engineering also at Carnegie Mellon University. Jun also worked as a software engineer at Samsung Electronics.