Kiwon Sohn

Kiwon Sohn headshot

Associate Professor

Electrical and Computer Engineering

College of Engineering, Technology, and Architecture
860.768.4894 UT 238 Department of Electrical and Computer Engineering Assistive Robot Team Website

PhD, Mechanical Engineering and Mechanics, Drexel University

MS, Electrical and Systems Engineering, University of Pennsylvania

BS, Electronic and Electrical Engineering, Kyungpook National University (South Korea)

Kiwon Sohn is currently an Associate Professor in the Samuel I. Ward Department of Electrical and Computer Engineering and Robotics Engineering Program Director in CETA, University of Hartford. He is leading ART (Assistive Robotics Team) and current research topics include 1) robot vision/perception system development and its data processing and 2) whole body motion design and optimization for vehicle handling tasks. Recently, he was serving as a Chief of Engineering (COE) in team DRC-HUBO@UNLV (Finalist Team in DARPA Robotics Challenge Finals 2015) and as a Chief Engineer in a robotics research center DASL@UNLV (Drones and Autonomous Systems Lab). He also served as a Task 1 Leader and a Hardware Manager of team DRC-HUBO (Drexel) in DRC Trials 2013.

Sohn received a PhD from MEM, Drexel University in 2014. Academic advisor was Paul Oh, former National Science Foundation (NSF) Robotics program Director. Research interests included machine learning, kinematics/dynamics and computer vision. Sohn received a Master of Science degree in School of Engineering and Applied Science, University of Pennsylvania - Philadelphia, PA in 2007. He worked in Kod*lab (GRASP Lab) and built a Sprawl Edubot which is Rhex class small hexapod robot (2006-2008). Sohn also worked in Robotics and Neural Systems Laboratory (RNSL), ECE, University of Arizona (2008-2011). He built a cognitive robotics architecture based on artificial personality and manufactured a human interactive robot.

For more details, please visit Sohn's personal research website here.

  • Robotics (Mobile, Multi-Legged and Humanoids)
  • Machine Learning (Supervised and Non-Supervised Deep Learning and Reinforcement Learning)
  • Computer Vision and Navigation of Driving Humanoids and Mobile Robots