Shengkai Zhang

Shengkai Zhang is an active researcher with 26 publications and 444 citations spanning engineering, computer science, and environmental disciplines. His work demonstrates strong interdisciplinary collaboration through co-authorship with researchers like Kezhong Liu and Mozi Chen across multiple high-impact venues including IEEE conferences, arXiv, and specialized journals.

His research interests center on Machine Learning applications in maritime systems, with significant contributions to Large Language Model integration for ship navigation, wireless sensing for bridge officer monitoring, and sensor fusion techniques. Additional expertise spans robotics perception (visual-inertial systems, mmWave radar enhancement), environmental modeling (urban energy systems, climate studies), and biomedical applications of traditional medicine. Recent work shows increasing focus on AI foundation models and their security implications.

Zhang's publication trajectory reveals consistent output with accelerating impact since 2023, featuring 15+ papers in 2024 alone. His research clusters around three core themes:

• Maritime AI Systems (LLM navigation, track association, watchkeeping monitoring)
• Advanced Sensing Technologies (mmWave radar, Wi-Fi sensing, GNSS fusion)
• Environmental & Biomedical Applications (urban energy modeling, gut microbiome studies)

These areas demonstrate both technical depth in signal processing/computer vision and practical focus on real-world engineering challenges.