Tutorials
TUTORIAL #1
  • "Advanced Deep Learning Methods for Autonomous Mobility"
  • Prof. Joongheon Kim
  • Korea University, Korea
Abstract

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.

Biography

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).

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