Dr. Lingfeng Zhang is currently a Lecturer at Hubei Minzu University, where he leads cross-disciplinary research applying artificial intelligence to real-world challenges. He received his Ph.D. in Electrical Engineering and Information Systems from The University of Tokyo. His primary research interests lie in the intersection of deep learning and signal processing, with a specific focus on robust and scalable multivariate time series classification. Dr. Zhang actively explores advanced AI methodologies, including novel classifier design, privacy-preserving learning (such as federated learning), data augmentation, and pre-training models. Furthermore, his academic pursuits extend to applied AI across diverse domains. He is deeply interested in decoding physiological signals (e.g., EEG and sEMG) for multimodal balance analysis and human motion assessment, as well as developing predictive models for hydrological runoff forecasting and hydropower optimization. Driven by a passion for bridging theoretical AI with practical systems, his broader technical expertise also spans graph convolutional networks, multi-agent systems, and computer vision.