The American Institute of Aeronautics and Astronautics (AIAA), the world’s largest aerospace professional society, has selected Worcester Polytechnic Institute (WPI) Raghvendra Cowlagi, associate professor, aerospace engineering, as a member of its Class of 2027 Associate Fellows.
The prestigious distinction recognizes individuals who have accomplished important engineering or scientific work, conducted original work of outstanding merit, or made significant contributions to the arts, sciences, or technology of aeronautics or astronautics. Only 17% of AIAA members are ever selected as Associate Fellows.
Cowlagi was recognized “for contributions to hierarchical path-planning, sensor configuration, and synthetic data generation in intelligent and autonomous aerial vehicles.”
“This recognition by a society of my peers in the aerospace engineering community feels like a sound validation of my contributions to research,” Cowlagi said. “I am especially happy about the recognition of my work on sensor configuration and path planning for uncrewed aerial vehicles (UAV). Over several years, my students and I have studied the problem of using unmanned aerial vehicle-mounted sensors to obtain data of the most relevance to decision-making. Starting nearly a decade ago with theoretical underpinnings, we have now worked our way to applications in search-and-rescue operations during escalating wildfires and floods.”
Cowlagi’s research advances autonomous UAV systems by connecting decision-making with the physical realities of flight. His work focuses on creating executable flight paths, helping airborne sensors collect the most mission-relevant information, and generating realistic aerospace data through physics-informed machine learning.
Cowlagi has developed planning methods that allow autonomous aircraft to adjust routes in real time, including in response to subsystem damage. Validated through a U.S. Air Force initiative in simulations by Aurora Flight Sciences, a Boeing company, the work could improve UAV safety and resilience. He also developed Context-Relevant Mutual Information, a framework that reduces the sensor data and flight time needed for effective mission planning. His team has applied the approach to wildfire data to explore improved sensor deployment for search-and-rescue operations. In addition, his physics-informed machine-learning methods generate realistic synthetic data to supplement limited real-world datasets and support the development of next-generation autonomous aerospace systems.
Cowlagi’s scholarship has appeared in leading publications including IEEE Transactions on Robotics, Automatica, and the AIAA Journal of Guidance, Control, and Dynamics. His research has been applied in collaboration with the aerospace industry and recognized through competitive honors including the Air Force Office of Scientific Research Young Investigator Program award.
In addition to his research, Cowlagi has been recognized for excellence in teaching. He has received WPI’s Romeo L. Moruzzi Award for Innovation in Undergraduate Education and the Morgan-Worcester Distinguished Instructorship Award for his ability to make complex theoretical concepts accessible and engaging for students.
