Dong Ho Lee

Multi-agent systems, optimization, games, and learning.

Austin, TX

The University of Texas at Austin

Research Assistant August 2023–present · Austin, Texas · Advisor: David Fridovich-Keil

  • Developing a Newton solver for real-time receding-horizon control of multiple robots with ordered objectives.
  • Developed Games of Ordered Preference (GOOP) to model strategic agents with strict objective hierarchies; published in IEEE Robotics and Automation Letters (2025).
  • Derived a compact reformulation of the GOOP optimality system, improving computational performance through the formulation itself. The 2026 preprint is under review at SIAM Journal on Optimization.
  • Co-developed methods for mixed Nash/Stackelberg games; the team evaluated the solver in simulation and on three ground robots with receding-horizon planning (WAFR 2026).
  • Developed a convex reformulation and branch-and-bound method for game-theoretic hierarchical routing (IEEE Control Systems Letters, 2025).
  • Contributed to research on approximate GOOP solutions (ITSC 2025) and strategic information gathering in noncooperative games (AAMAS 2025).

KAIST — Strategic Aerospace Initiative

Research Assistant September 2020–July 2023 · Daejeon, South Korea · Advisor: Jaemyung Ahn

  • Built a Transformer-based reinforcement learning policy for multi-UAV mission replanning under time and fuel constraints.
  • Developed data-efficient policy training and evaluated the method on team orienteering, traveling salesperson, and capacitated vehicle routing problems (Applied Intelligence, 2024).
  • Developed deep residual networks for predicting changes in aerodynamic coefficients under extreme flow conditions (Aerospace Science and Technology, 2023).

Agency for Defense Development

Research Officer / First Lieutenant · Aerospace Technology Research Institute June 2017–May 2020 · Daejeon, South Korea

  • Developed multi-UAV mission-planning methods using mixed-integer programming with Gurobi and genetic algorithms.
  • Implemented mission-planning software for a Linux-based UAV simulator.
  • Co-inventor of KR102063851, a registered patent for optimization of unmanned vehicle mission allocation (January 2020).

KAIST — Unmanned Systems Research Group

Undergraduate Research Intern December 2015–September 2016 · Advisor: David Hyun-chul Shim

  • Designed and constructed a 3D-printable modular drone with self-configuration capability.
  • Received the Excellent Research Award in KAIST’s Undergraduate Research Participation program.

Education

Degree Institution Dates
Ph.D., Aerospace Engineering UT Austin August 2023–May 2028 (expected)
M.S., Aerospace Engineering KAIST September 2020–August 2022
B.S., Aerospace Engineering, magna cum laude KAIST September 2012–February 2017

M.S. thesis: Multiple UAV Routing for Re-planning under Dynamic Environment using Deep Reinforcement Learning.

Technical skills

  • Programming: Python, Julia, C/C++, MATLAB.
  • Tools: PyTorch, NumPy, Gurobi, CUDA, Linux.
  • Planning and optimization: trajectory optimization, receding-horizon control, Newton methods, constrained and multilevel optimization, and mixed-integer programming.
  • Games and learning: Nash and Stackelberg games, deep reinforcement learning, and Transformers.

Teaching, talks, and selected honors

  • Invited seminar, “Multiple UAV Re-planning using Deep Reinforcement Learning,” Agency for Defense Development, November 2022.
  • Teaching: AE201 Introductory Flight Project (2021); advanced English writing and reading (2015–2016); LG-KAIST Science Camp (2015); Academic English Camp (2014–2015).
  • Excellent Paper Award, Avionics Systems Symposium Korea (2018).
  • KAIST Excellent Research Award in URP, Excellence in Leadership & Volunteer Activity, and College of Engineering Dean’s List (2016).