Dr. Tianyu Gao | Fault Diagnosis | Excellence in Research Award

Harbin Institute of Technology | China

Tianyu Gao is a researcher from China whose work centers on advancing intelligent operation and maintenance technologies through AI-driven methods, with strong contributions to multimodal information fusion, intelligent sensing, and autonomous decision-making for unmanned equipment. He completed comprehensive academic training in measurement and control, instrumentation, and information and communication engineering at the Harbin Institute of Technology, developing a solid foundation that supports his interdisciplinary research. His professional experience spans postdoctoral and grant-supported research focused on improving the reliability of high-performance marine and unmanned propulsion systems by exploring robust diagnostic models and real-time health assessment strategies based on transfer learning and dynamic monitoring. He has contributed notable publications in leading journals covering complex system fault diagnosis, spatiotemporal attention networks, multimodal fusion, and intelligent fault detection. His research skills include advanced machine learning, deep learning, intelligent sensing, signal processing, autonomous diagnostic modeling, and system health monitoring, complemented by strong experience in scholarly reviewing for major IEEE and domain-leading journals. He has served as a session chair at prominent international conferences and is an active member of several IEEE societies. Overall, his work reflects a commitment to advancing intelligent equipment technologies, fostering innovation in autonomous systems, and contributing impactful research to the global engineering community.