Huy T Tran

For More Information

• Group website

Education

• Ph.D. in Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, December 2015
• M.S. in Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, May 2014
• M.S. in Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, May 2010
• B.S. in Mechanical Engineering, North Carolina State University, Raleigh, NC, Magna Cum Laude May 2008

Academic Positions

• Assistant Professor, Applied Research Institute, Aug. 2021 - present (0%)
• Assistant Professor, University of Illinois at Urbana-Champaign, Department of Aerospace Engineering, Aug. 2021 - present (100%)
• Visiting Scholar, Air Force Institute of Technology, Department of Electrical and Computer Engineering, May 2018 - June 2018.
• Research Assistant Professor, Applied Research Institute, Jan. 2017 - July 2021 (0%)
• Research Assistant Professor, University of Illinois at Urbana-Champaign, Department of Aerospace Engineering, Jan. 2017 - July 2021 (100%)
• Adjunct Research Assistant Professor, University of Illinois at Urbana-Champaign, Department of Aerospace Engineering, Dec. 2016 - Jan. 2017 (0%)

Other Professional Employment

• Sr. Multi-Disciplinary Systems Engineer, The MITRE Corporation, Bedford MA, Jan. 2016 - Jan. 2017

Major Consulting Activities

• Consultant for The MITRE Corporation, Feb. 2017-Sept. 2017

Resident Instruction

• AE 498 CSE/CSO: Computational Systems Engineering (Spring 2017, Spring 2019, Spring 2025).
• AE 443: Aerospace Systems Design II (Spring 2018).
• AE 442: Aerospace Systems Design I (Fall 2017).
• AE 370: Aerospace Numerical Methods (Spring 2022, Fall 2022, Fall 2023, Fall 2024).
• AE 202: Introduction to Flight Mechanics (Fall 2018, Fall 2019, Fall 2021).
• AE 199: Aerospace Computing (Spring 2020, Fall 2020).

Research Interests

• Autonomy
• Reinforcement Learning
• Artificial Intelligence and Machine Learning
• Robotics
• Multiagent Systems
• Intelligent Transportation Systems
• Critical Infrastructure Resilience

Selected Articles in Journals

• M. Yuasa, H. T. Tran, and R. S. Sreenivas, "On Generating Explanations for Reinforcement Learning Policies: An Empirical Study", IEEE Control System Letters, 8, p. 3027-3032 (2024).
• M. V. Gasparino, A. N. Sivakumar, Y. Liu, A. E. B. Velasquez, V. A. H. Higuti, J. Rogers, H. T. Tran, and G. Chowdhary, "WayFAST: Traversability Predictive Navigation for Field Robots", IEEE Robotics and Automation Letters, 7(4), p. 10651-10658 (2022).
• A. Markina-Khusid, R. Jacobs, L. Antul, L. Cho, and H. T. Tran, "A Complex Network Framework for Validated Assessments of Robustness in Systems of Systems", IEEE Systems Journal, 16(1), p. 1092-1102 (2022).
• J. Heglund, K. Hopkinson, and H. T. Tran, "Social Sensing: Towards Social Media as a Sensor for Resilience in Power Systems and Other Critical Infrastructures", Sustainable and Resilient Infrastructure, 6(1-2), p. 94-106 (2021).
• A Wong, S. Tan, K. R. Chandramouleeswaran, and H. T. Tran, “Data-driven Analysis of Resilience in Airline Networks”, Transportation Research Part E: Logistics and Transportation Review, 143 (2020).
• K. Thompson and H. T. Tran, "Operational Perspectives into the Resilience of the U.S. Air Transportation Network Against Intelligent Attacks", IEEE Transactions on Intelligent Transportation Systems, 21(4), p. 1503-1513 (2020).
• N. Napier, S. Sriraman, H. T. Tran, and K. James, "An Artificial Neural Network Approach to Generating High-Resolution Designs in Topology Optimization", Journal of Mechanical Design, 142(1), p. 1-11 (2020).
• H. T. Tran, J. C. Domercant, and D. N. Mavris, "Parametric Design of Resilient Complex Networked Systems", IEEE Systems Journal, 13(2), p. 1496-1504 (2019).
• Tran, H. T., Balchanos, M., Domercant, J. C., & Mavris, D. N., "A framework for the quantitative assessment of performance-based system resilience," Reliability Engineering and System Safety, 158, p. 73-84 (2017).
• H. T. Tran, J. C. Domercant, and D. N. Mavris, "Evaluating the Agility of Adaptive Command and Control Networks from a Cyber Complex Adaptive Systems Perspective," Journal of Defense Modeling & Simulation, 12, p. 405-422 (2015).

Articles in Conference Proceedings

• R. Nigam, J. Choi, N. Parikh, M. Z. Li, and H. T. Tran, “A Survey of Current Applications of Inverse Reinforcement Learning in Aviation and Future Outlooks", AIAA SCITECH 2025 Forum, Orlando, FL (2025).
• N. Parikh, R. Nigam, M. Z. Li, and H. T. Tran, “Inefficiency of Self-Interested Behaviour in Markov Games: State-Dependent Price of Anarchy", AIAA SCITECH 2025 Forum, Orlando, FL (2025). Finalist for the ISTC SciTech Student Paper competition.
• W. Dimon, N. Chase, N. Van Stralen, R. Nigam, M. Lembeck, and H. T. Tran, "D-AnoGAN: Anomaly Detection in Disconnected Data Manifolds with Generative Adversarial Networks", 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy (2022).
• S. H. Kim, N. V. Stralen, G. Chowdhary, and H. T. Tran, "Disentangling Successor Features for Coordination in Multi-agent Reinforcement Learning," Proc. of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022), Virtual (2022). 27% full paper acceptance rate.
• J. Heglund, P. Taleongpong, S. Hu, and H. T. Tran, “Railway Delay Prediction with Spatial-Temporal Graph Convolutional Networks”, 2020 IEEE Intelligent Transportation Systems Conference (ITSC), Virtual (2020).
• N. V. Stralen, S. Kim, H. T. Tran, and G. Chowdhary, "Evaluating Adaptation Performance of Hierarchical Deep Reinforcement Learning", 2020 International Conference on Robotics and Automation (ICRA), Virtual (2020). 44% acceptance rate.
• K. Thompson and H. T. Tran, “Application of a Defender-Attacker-Defender Model to the U.S. Air Transportation Network,” 2018 IEEE International Symposium on Technologies for Homeland Security, Woburn, MA (2018).
• K. R. Chandramouleeswaran and H. T. Tran, "Data-driven Resilience Quantification of the US Air Transportation Network," 2018 Annual IEEE International Systems Conference (SysCon), Vancouver, CA (2018).
• L. Antul, S. Ricks, L. Cho, M. Cotter, R. B. Jacobs, A. Markina-Khusid, J. Kamenetsky, J. Dahmann, and H. T. Tran, "Toward Scaling Model-Based Engineering for Systems of Systems," 2018 IEEE Aerospace Conference, Big Sky, MT (2018).
• K. R. Chandramouleeswaran, D. Krzemien, K. Burns, and H. T. Tran, “Machine Learning Prediction of Airport Delays in the US Air Transportation Network,” 2018 AIAA Aviation Forum, Atlanta, GA (2018).
• H. T. Tran, J. C. Domercant, and D. N. Mavris, "Designing Resilient System-of-Systems Networks", 2017 Annual IEEE International Systems Conference (SysCon), Montreal, CA (2017).
• H. T. Tran, J. C. Domercant, and D. N. Mavris, "A Network-based Cost Comparison of Resilient and Robust System-of-Systems", In Procedia Computer Science (Vol. 95, p. 126-133), Complex Adaptive Systems Conference, Los Angeles, CA (2016). Best Paper Award: Theoretical.
• H. T. Tran, J. C. Domercant, and D. N. Mavris, "A System-of-Systems Approach for Assessing the Resilience of Reconfigurable Command and Control Architectures", AIAA Infotech @ Aerospace Conference, Kissimmee, FL, (2015).
• M. G. Balchanos, J. C. Domercant, H. T. Tran, and D. N. Mavris, "Metrics-based Analysis and Evaluation Framework for Engineering Resilient Systems", 2014 7th International Symposium on Resilient Control Systems (ISRCS), p. 1-7, Denver, CO (2014).
• H. T. Tran, J. C. Domercant, and D. N. Mavris, "Trade-offs Between Command and Control Architectures and Force Capabilities Using Battlespace Awareness", 19th International Command and Control Research and Technology Symposium (ICCRTS), Alexandria, VA (2014).

Refereed Conference Papers and Presentations

• J. Heglund and H. T. Tran, “Coordination Machines for Minimizing Communication in Multi-Agent Reinforcement Learning", Coordination and Cooperation in Multi-Agent Reinforcement Learning (CoCoMARL) RLC 2024 Workshop, Amherst, MA (2024).
• R. Nigam, N. Parikh, J. Heglund, M. Yuasa, and H. T. Tran, “Coordination in Ad Hoc Teams with Generalized Policy Improvement", IROS 2023 Workshop on Advances in Multi-Agent Learning - Coordination, Perception, and Control, Detroit, MI (2023). Selected for oral presentation.
• N. Van Stralen, S. Kim, H. T. Tran, and G. Chowdhary, "Feature Specialization and Clustering Improves Hierarchical Sub-task Learning", Proc. of the Adaptive and Learning Agents Workshop (ALA 2022), Virtual, 2022.