The AIAA Intelligent Systems Technical Committee (ISTC) is concerned with the application of Intelligent System (IS) technologies and methods to aerospace systems, the verification and validation of these systems, and the education of the AIAA membership in the use of IS technologies in aerospace and other technical disciplines.
The Intelligent Systems Technical Committee addresses the application of Intelligent System (IS) technologies and methods to aerospace systems, the verification and validation of these systems, and the education of the AIAA membership in the use of IS technologies in aerospace and other technical disciplines.
ISTC Focus: Commercial and military aerospace systems, and those ground systems that are part of test, development, or operations of aerospace systems. Technologies which enable safe and reliable operation of complex aerospace systems or sub-systems with minimal or no human intervention (autonomy), or collaborative synthetic-human agent teams are of interest. These include, but are not limited to: autonomous and expert systems, discrete planning/scheduling algorithms, intelligent data/image processing, learning and adaptive techniques, data fusion and reasoning, and knowledge engineering.
Members of the ISTC have experience in developing and managing aerospace systems involving knowledge engineering, knowledge acquisition, verification/validation of knowledge based systems, neural networks, and expert systems, as well as the use of artificial intelligence concepts/techniques to support natural language interfaces, image understanding, planning/scheduling, and data fusion.
If you would like more information, or are interested in joining the ISTC, please see the Our Mission page!
Yearly workshop
2026 Intelligent Systems Workshop
Digital Transformation Center, August 12-13, 2026 (save the date!)
The 10th annual Intelligent Systems Workshop will take place August 12-13, 2026, at the Digital Transformation Center in Dayton, OH.
This year’s workshop themes:
Autonomy at Scale
Human Interaction
Counter UAS
Precision Navigation
AI's Impact on Workforce
Registration is open! ($20 Landing Fee until August 1st, then $75) Register here!
(Includes lunch on Day 1. No fee for student posters.)
Abstract: Multi-agent systems can help with many real-world problems, such as using drones for wildfire management. Multi-agent reinforcement learning (MARL) has shown promise for optimizing decision polices for such systems. However, current algorithms do not scale well to large systems, struggle to adapt to new ad hoc teams, and have limited guarantees. In this talk, I will first discuss our work exploring the bias-variance tradeoff for scaling MARL to large teams. I will then present an algorithm for agents to adapt to new teammates with no online learning. Finally, I will introduce work integrating contraction theory with RL to provide stability guarantees.
Bio: Huy T. Tran is an Assistant Professor in theAerospace Engineering department at the University of Illinois Urbana-Champaign, Urbana, IL. He received a BS degree from North Carolina State University, Raleigh, NC in 2008, and an MS degree from the University of Wisconsin–Madison, Madison, WI, in 2010, both in Mechanical Engineering. He received MS and PhD degrees in Aerospace Engineering from the Georgia Institute of Technology, Atlanta, GA, in 2014 and 2015. He has also worked as a multi-disciplinary systems engineer for the MITRE Corporation. He is the director of the Laboratory for Intelligent Robots and Agents (LIRA) at Illinois. His research currently focuses on autonomy in uncertain and unstructured environments, with an emphasis on reinforcement learning, multi-agent systems, and controls applied to robotics and intelligent transportation systems. He is an affiliate of the University of Illinois Coordinated Science Laboratory, Center for Autonomy, Illinois Robotics Group, and Smart Transportation Infrastructure Initiative. He is a member of IEEE, the IEEE Multi-Robot Systems Technical Committee, and AIAA.
News: 2026 May 26, 2:50pm EDT (by Cat McGhan)
ISTC Technical Seminar Series
Don’t miss Dr. Peng Jiang’s seminar this Wednesday May 27 at 1:00pm ET on Zoom!
Speaker: Peng Jiang, PhD
University of Nebraska at Omaha
Date/time: Wednesday, May 27th, 2026 – 1:00pm-2:00pm Eastern time
Title: Enhancing UAV Localization and Security in GPS-Challenged Environments
Abstract: Unmanned aerial vehicles (UAVs) rely on GPS and wireless communications for navigation and coordination, making them vulnerable to spoofing, signal manipulation, and other attacks. This talk presents a multi-modal approach to UAV security, fusing data from cameras, LiDAR, IMUs, geospatial maps, and wireless signals to detect compromised positioning and communication channels. The research highlights emerging UAV attack surfaces and demonstrates how integrated sensing and AI-driven analysis can enhance resilience, operational safety, and trustworthiness in adversarial or GPS-challenged environments.
Bio: Dr. Peng Jiang is an Assistant Professor of Cybersecurity at the University of Nebraska at Omaha. His research focuses on securing UAVs, autonomous vehicles, and other cyber–physical systems by developing intelligent, multi-modal methods for detecting and mitigating emerging threats in navigation, wireless communications, and perception. He holds a B.S. in Communication Engineering (Chongqing University of Posts and Telecommunications) and an M.E. and Ph.D. in Electrical and Computer Engineering (Old Dominion University). He has received the NSF CRII Award, the UNO Research and Creative Activity Award, and the PEARC 2025 Best Paper Award.
News: 2026 Mar 16, 4:23pm EDT (by Cat McGhan)
ISTC Technical Seminar Series
Don’t miss Dr. Jueming Hu’s seminar Wednesday March 18 at 1:00pm ET on Zoom!
Speaker: Jueming Hu, PhD
University of North Dakota
Date/time: Wednesday, March 18th, 2026 – 1:00pm-2:00pm Eastern time
Title: AI-Assisted UAV Operations for Safe, Resilient, and Efficient Autonomy
Abstract: As Artificial Intelligence (AI) continues to advance, autonomous systems such as unmanned aerial vehicles (UAVs) and robotics are rapidly gaining capabilities in complex, safety-critical environments. This talk will present a three-level view of AI-assisted UAV operations, spanning vehicle control, mission-level resilience, and multi-agent coordination. At the vehicle level, I combine reinforcement learning and optimization to achieve safe and steady autonomous landing for fixed-wing aircraft. At the mission level, I develop learning-based methods to detect and mitigate GPS spoofing, with an emphasis on mission continuity under cyber attacks. At the multi-agent level, I integrate reinforcement learning with formal methods to improve learning efficiency for temporally extended missions and team behaviors. Building on these results, this talk will outline ongoing research directions toward trustworthy autonomous systems, including data-driven dynamics modeling and state estimation, control and planning in unseen environments, and runtime monitoring and mitigation.
Bio: Dr. Jueming Hu is an assistant professor in the Department of Mechanical Engineering at the University of North Dakota (UND). She received her Ph.D. and M.S. degrees in Mechanical Engineering from Arizona State University, as well as a B.S. degree in Mechanical Engineering from Southeast University, China. Before joining UND, she was a postdoctoral researcher at Texas A&M University and Arizona State University. Dr. Hu’s research interests include AI, optimization, and formal methods and their applications in cyber-physical systems.
More announcements are available on the Announcements page.
