Mia Y. Wang
Assistant Professor
Education
PhD Technology, Purdue University
MS Computer and Information Technology, Purdue University
MA in Music Performance, Central Michigan University
BA Music Performance, Tongji University, China
Research Interest
Dr. Wang’s research focuses on developing intelligent autonomous drone systems that integrate machine learning and artificial intelligence (AI) to enhance adaptability, reliability, and perception in complex environments. She explores advanced navigation strategies for aerial and underwater drones, particularly in scenarios where traditional positioning systems are unreliable or unavailable. Her work emphasizes multimodal sensing—combining acoustic signals, computer vision, and environmental data—to improve detection, tracking, and situational awareness. A central theme of her research is enabling seamless operation across air and water domains, with applications in environmental monitoring, logistics, and security. As the director of the Drone Lab at the ºÚÁϳԹÏÍø, Dr. Wang leads interdisciplinary collaborations, mentors student researchers, and partners with industry to translate AI-driven innovations into practical solutions that address real-world challenges.
Courses Taught
CSCI 220 - Computer Programming I
CSCI 230 - Data Structures and Algorithms
DATA 507 - Scientific Computing in Data Science
Selected Publications
Berg A., Zhang Q., and Wang M. 2025. 4,500 Seconds: Small Data Training Approaches for Deep UAV Audio Classification. Accepted to publish in the 14th International Conference on Data Science, Technology and Applications (DATA).
Zhang Q., Johnson D., Jensen M., Fitzgerald C., Ramirez D. and Wang M. 2025). Optimizing Automotive Inventory Management: Harnessing Drones and AI for Precision Solutions. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1140-1145.
Kim, J., Zhang, Q., Matson, E.T. and Wang, M.Y., 2024, December. Improving Drone Classification with Audio-Derived Visual Features: A Vision Model Comparison. In 2024 Eighth IEEE International Conference on Robotic Computing (IRC) (pp. 41-45). IEEE.
Wang, M.Y., Chu, Z., Entzminger, C., Ding, Y. and Zhang, Q., 2024. Visualization and interpretation of mel-frequency cepstral coefficients for uav drone audio data. In Proceedings of the 13th International Conference on Data Science, Technology and Applications-DATA, INSTICC. SciTePress (pp. 528-534).