Specialty
Robust AI
Trustworthy LLM Reasoning
Contact Us
Dr. Longwei Wang is an Assistant Professor in the Department of Computer Science and Vice Director of the AI Research Lab at the University of South Dakota. He received his Ph.D. in Computer Science and Software Engineering from Auburn University.
His research focuses on explainability, robustness, and generalization—and their interactions—in building trustworthy AI systems. He aims to develop algorithms that make machine learning models not only explainable but also robust and efficient. In addition, he investigates trustworthy reasoning in large language models, with the goal of improving reliability, interpretability, and safety in real-world applications. His work has been published in leading venues such as NeurIPS, AAAI, ICDM, IEEE CAI, IEEE INFOCOM, GLOBECOM, and IEEE Transactions. He has also been recognized with the Best Paper Award at ISPR 2025.
As an active member of the research community, he has served as a Special Track and Workshop Chair for the International Conference on Recent Trends in Image Processing and Pattern Recognition and as a Program Committee member for major AI conferences, including ICLR, AAAI, IJCAI, IEEE CAI, and IEEE CogMI. Dr. Wang regularly serves as a reviewer for high-impact journals such as ACM Transactions on Intelligent Systems and Technology, IEEE Transactions on Artificial Intelligence, IEEE Transactions on Systems, Man, and Cybernetics.
CSC 488/588 Pattern Recognition and Machine Learning
CSC 525 High Performance Computing
CSC 790 Seminar: Advanced topics in deep learning
CSC 492/592 Internet of Things
Robust and Explainable Artificial Intelligence, Trustworthy Deep Learning, Equivariance, Disentanglement, Compositionality, Explainable AI for Medical analysis, and XAI for Science
