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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 AI explainability, robustness, and security, and their interactions in building trustworthy AI systems. He aims to develop algorithms that make machine learning models not only interpretable but also robust to adversarial attacks. His collaborators and he developed Winsor-CAM, an interpretable visual explanation method published in IEEE TPAMI. He has also been developing theoretical foundations linking explainability with adversarial robustness, with results appearing in NeurIPS and IEEE ICDM. His work has been published in leading venues such as NeurIPS, TPAMI, ICDM, AAAI, ACM Web Conference, and several IEEE journals, and he received 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, WWW, 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. He is an IEEE and ACM member.
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, Compositionality, Explainable AI for Medical analysis, and XAI for Science
