Welcome!

I am an Assistant Professor in the Aerospace and Ocean Engineering Department at Virginia Tech.

I received my Ph.D. in Autonomous Systems from the University of Colorado Boulder, where I was advised by Prof. Zachary Sunberg and Prof. Morteza Lahijanian.

My goal is to enable autonomous systems to complete tasks while providing guarantees on their safety and operational properties. Recently, my focus has been on developing theoretically sound and practically efficient algorithms for decision-making under uncertainty. This often involves combining techniques from formal methods (temporal logics, hybrid systems), probabilistic modeling and planning (MDPs, POMDPs, Stochastic Games), and reinforcement learning. My research finds applications in diverse areas including self-driving cars, uncrewed aerial vehicles, spacecraft and space robots, underwater vehicles, smart grids, and operations.

Experience

Education

• Ph.D in Aerospace Engineering Sciences (Autonomous Systems), University of Colorado Boulder, 2025
• M.S. in Aerospace Engineering Sciences (Autonomous Systems), University of Colorado Boulder, 2023
• B.S. in Mechanical Engineering, National University of Singapore, 2019

Appointments

• 2026-Present: Assistant Professor
  • Virginia Tech, Aerospace and Ocean Engineering
• 2024-2025: Visiting Student Researcher
  • NASA Jet Propulsion Laboratory
• 2020-2025: Graduate Research Assistant
  • University of Colorado Boulder
• 2019-2020: Research Engineer
  • Future Urban Mobility, Singapore-MIT Alliance for Research and Technology

Research: Assured Autonomous Systems

My research focuses on designing Assured Autonomous Systems that operate safely and reliably under uncertainty, partial observability, and incomplete information. I am broadly interested in theoretical analysis, designing efficient algorithms, practical techniques for:

• Decision-making under uncertainty (MDPs, POMDPs, Games)
• Constrained and risk-aware planning
• Formal synthesis and verification
• Reinforcement learning for partially observable systems
• Data-driven models in planning
• Multi-layered autonomoous system architectures
• Integrated Task and Motion planning under uncertainty
• Temporally extended tasks (Temporal Logic specifications, Non-Markovian objectives, long horizon sparse reward problems)
• Applications in robotics and space systems

Recent Publications

Below are a few recent highlights. For a complete list and access to all papers, please visit my Publications page.

1. Perrault, N., Ho, Q. H., & Lahijanian, M. (2025). Kino-PAX: Highly Parallel Kinodynamic Sampling-based Planner. Robotics and Automation Letters (RA-L).
2. Muvvala, K., Ho, Q. H., & Lahijanian, M. (2025). Beyond Winning Strategies: Admissible and Admissible Winning Strategies for Quantitative Reachability Games. International Joint Conference on Artificial Intelligence (IJCAI).
3. Ho, Q. H., Feather, M., Rossi, F., Sunberg, Z., & Lahijanian, M. (2024). Sound and Efficient Algorithms for POMDPs with Reachability Objectives via Heuristic Search. Conference on Uncertainty in Artificial Intelligence (UAI).
4. Ho, Q. H., Becker, T., Kraske, B., Laouar, Z., Feather, M., Rossi, F., Sunberg, Z., & Lahijanian, M. (2024). Recursively-Constrained Partially Observable Markov Decision Processes. Conference on Uncertainty in Artificial Intelligence (UAI).
5. Ho, Q. H., Sunberg, Z., & Lahijanian, M. (2023). Planning with SiMBA: Motion Planning under Uncertainty for Temporal Goals using Simplified Belief Guides. IEEE International Conference on Robotics and Automation (ICRA).
6. Ho, Q. H., Sunberg, Z., & Lahijanian, M. (2022). Gaussian Belief Trees for Chance Constrained Asymptotically Optimal Motion Planning. IEEE International Conference on Robotics and Automation (ICRA).
7. Luo*, Y., Meghjani*, M., Ho*, Q. H., Hsu, D., & Rus, D. (2021). Interactive Planning for Autonomous Urban Driving in Adversarial Scenarios. IEEE International Conference on Robotics and Automation (ICRA).

View All Publications