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Faculty, Staff & Researchers

Jeongjoon Gwon

Jeongjoon Gwon
Jeongjoon Gwon
  • Student Assistant (Researcher), Urban Freight Lab
  • Undergraduate Student, Industrial & Systems Engineering and Computer Science, KAIST (Korea Advanced Institute of Science and Technology)
jgwon@uw.edu
  • Supply Chain Network Optimization
  • Operations Research for Logistics Systems
  • Urban Freight and Last-Mile Delivery
  • Data-Driven Transportation Systems

Jeongjoon supports research on urban freight systems and curb management at the Urban Freight Lab, including the SMART Curb Loading Zone (CVLZ) Data Program and Low Pollution Neighborhood (LPN) freight strategy research. His work focuses on integrating and analyzing curb regulation and loading zone datasets across multiple U.S. cities to better understand urban freight operations and support data-driven transportation policy and planning.

  • Presidential Science Scholarship (Korea) – Recognized as one of Korea’s top STEM students; awarded a national full-tuition scholarship
  • IPESK Future Engineer Selection – Selected by the Institute for Promotion of Engineering and Science Korea as an outstanding undergraduate engineer
  • Undergraduate Research Grant – Advanced Manufacturing Challenge, KAIST
  • Social Value Award (3rd Place), Tech for Impact Project – “WheelCity,” an accessibility mapping platform using computer vision and community-driven data
  • Army Commendation Medal – U.S. Army
  • B.S., Industrial & Systems Engineering, Double Major: Computer Science, KAIST (Korea Advanced Institute of Science and Technology) (expected February 2027)
  • High School Diploma, Sejong Academy of Science and Arts, Sejong, South Korea (February 2020)

Jeongjoon Gwon is an undergraduate student at KAIST (Korea Advanced Institute of Science and Technology), double majoring in Industrial and Systems Engineering and Computer Science. His work focuses on applying optimization and operations research to large-scale logistics and supply chain systems. He has a particular interest in developing data-driven models and algorithms to improve the efficiency and resilience of complex logistics networks, including urban freight and last-mile delivery systems.

At the Urban Freight Lab, he contributes to research on curb management and urban freight policy, including the SMART Curb Loading Zone (CVLZ) Data Program and the Low Pollution Neighborhood (LPN) freight strategy project.

Previously, he conducted research on project scheduling optimization using genetic algorithms and on simulation-based evaluation of electric vehicle manufacturing systems.