Specialty
Explainable AI
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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 interdisciplinary expertise spans robust deep learning, explainable artificial intelligence (XAI), signal processing and wireless networks, allowing him to approach AI research from both theoretical and applied perspectives.
Dr. Wang’s research is dedicated to building robust and interpretable AI systems. He emphasizes the integration of equivariance-enforcing neural architectures with explainability-guided refinement strategies to improve adversarial robustness and ensure consistency in feature attribution. His work aims to advance the theoretical understanding of neural representations while also addressing practical challenges in model generalization and trustworthiness. In addition to his core focus on neural network robustness, he also explores representation learning for multimodal sensor fusion and the optimization of wireless networks through large-dimensional analysis.
His research has been published in leading journals and conferences, including IEEE Transactions on Vehicular Technology, IEEE Access, Elsevier Neurocomputing, IEEE INFOCOM, GLOBECOM, ICC, IEEE CAI, and CogMI. As an active member of the research community, Dr. Wang regularly reviews for high-impact journals such as IEEE Transactions on Artificial Intelligence, IEEE Transactions on Systems, Man, and Cybernetics, International Journal of Pattern Recognition and Artificial Intelligence, International Journal of Machine Learning and Cybernetics, Elsevier Pattern Recognition, IEEE Internet of Things Journal, and IEEE Transactions on Cognitive Communications and Networking.
At the University of South Dakota, Dr. Wang teaches both undergraduate and graduate courses, including Pattern Recognition and Machine Learning, High Performance Computing, and Internet of Things. He is also engaged in departmental and institutional service, contributing to faculty recruitment efforts and research symposia, such as the NSF Workshop on AI-Powered Materials Discovery. Additionally, he serves on the technical program committees for major AI conferences, including IEEE CAI and CogMI.
Dr. Wang maintains active collaborations with national and international researchers on advancing trustworthy and explainable AI across scientific and engineering domains. His research is supported by USD and contributes to the broader goal of building safe, interpretable, and resilient machine learning systems for real-world deployment.
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, Deep Learning, Adversarial Robustness, Symmetry-Enforced Neural Networks, Interpretability-Guided Model Refinement, Multimodal Representation Learning, and Trustworthy AI for Scientific Applications.
