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Sherman, M., Shao, S., Sun, X. and Zheng, J., 2024. Counter uav swarms: Challenges, considerations, and future directions in uav warfare. IEEE Wireless Communications. 

Mannan, F., Moore, L., Quiroga, J., Wietharn, A., Shao, S., Sun, X. and Hassanalian, M.. "Bio-Inspired Vertiport System Design for Supporting Drone Swarms in Methane Gas Detection from Orphaned Wells". Accepted to appear in ASNT Materials Evaluation, Special Issue: Bio-inspired Robotics and Sensing for NDT.

Mannan, F., Moore, L., Shao, S., Sun, X. and Hassanalian, M., 2024. Sustainable and Portable Vertiports Enabling Autonomous Drone Swarm Inspection in the Oil and Gas Industry. In AIAA AVIATION FORUM AND ASCEND 2024 (p. 3888). 

Mannan, F., Quiroga, J., Shao, S. and Hassanalian, M., 2025. April-Tag Detection via Deep Learning CNN Models. In AIAA SCITECH 2025 Forum (p. 0455). 

Mannan, F. and Hassanalian, M., 2024. Feasibility for a Solar Powered Autonomous Drone Vertiport System. In 2024 Regional Student Conferences (p. 86042). 

Yu, L., Li, Z., Yao, J. and Sun, X., 2024, June. Transformer-based multi-agent reinforcement learning for multiple unmanned aerial vehicle coordination in air corridors. In 2024 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 505-510). IEEE. 

Manu, D., Yao, J., Liu, W. and Sun, X., 2024. GraphGANFed: A federated generative framework for graph-structured molecules towards efficient drug discovery. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(2), pp.240-253.  

Yu, L., Sun, X., Albelaihi, R. and Yi, C., 2023. Latency-aware semi-synchronous client selection and model aggregation for wireless federated learning. Future Internet, 15(11), p.352. 

Pierre, J.E., Sun, X. and Fierro, R., 2024. Learning safe multi-uav coordination with temporal-spatial constraints. In AIAA SCITECH 2024 Forum (p. 0530). 

Li, Z., Hollenbeck, D., Sherman, M., Shao, S., Sun, X. and Hassanalian, M.. "Multi-agent reinforcement learning for UAV-based chemical plume source localization". IEEE Transactions on Intelligent Transportation Systems, under 2nd round review.

Chemical Plume Source Localization: https://github.com/Shao-wireless-lab/CPSL-Sim. The deep reinforcement learning codes guide multiple drones in a centralized training and decentralized execution manner. Drones identify the location of the emitter through search, tracing and declaration phases.