Google Scholar
Education
PhD, Computer Science, University of Virginia
About
Meiyi Ma is an Assistant Professor of Computer Science and a faculty member of the Institute for Convergent Software Integrated Systems at Vanderbilt University. Her research focuses on developing trustworthy, reliable, and explainable AI-enabled cyber-physical systems by integrating formal methods with machine learning. Her work develops rigorous methods for intelligent systems operating in complex, safety-critical environments, with applications in emergency response, public safety, transportation, smart cities, and healthcare. A central theme of her research is translating advances in AI into real-world societal impact. She collaborates with public agencies and community partners to co-develop and deploy AI-enabled systems that address real-world challenges, particularly in emergency response and civic infrastructure. Her social-impact research has been published at leading AI conferences, including AAAI, IJCAI and ICML. Dr. Ma’s work has been recognized with an NSF CAREER Award, Google Academic Research Awards, Best Paper and Best Artifact Awards at ACM/IEEE CHASE and ACM/IEEE ICCPS, selection as an EECS Rising Star, and the VUSE Community Impact Research Award.
Research Focus
Trustworthy and explainable artificial intelligence
Formal methods-guided machine learning
Cyber-physical systems for public safety, transportation, smart cities, and healthcare
Publication Highlights
Chen, Z., An, Z., Reynolds, J., Mullen, K., Martini, S., and Ma, M. “LogiDebrief: A Signal-Temporal Logic Based Automated Debriefing Approach with Large Language Models Integration.” Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2025.
He, G., An, Z., and Ma, M. “Formal Logic Inference Guided Uncertainty Quantification for Personalized Federated Learning.” Journal of Artificial Intelligence Research, vol. 86, article 21, 2026.
An, Z., Wang, X., Baier, H., Chen, Z., Dubey, A., Mukhopadhyay, A., Johnson, T. T., Sprinkle, J., and Ma, M. “LogiEx: Integrating Formal Logic and LLMs for Explainable Transit Planning.” Proceedings of the 17th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2026, pp. 88–99.