Jianhan Wu has graduated from the University of Science and Technology of China (USTC) with a master degree. He currently works as an Algorithm Engineer, and has long-term research and industrial-implementation experience in LMs, embodied intelligence, privacy-preserving computing. His research interests cover lightweight adaptation of large models, multimodal perception and understanding, optimization of embodied-intelligence systems, as well as edge-cloud collaborative inference. In terms of research projects, he has served as a key core member participating in multiple provincial-municipal key research tasks. For the Key R&D Program of Guangdong Provincial Department of Science and Technology, he tackles key technologies including on-device model deployment and edge-cloud collaborative intelligent computing. For the Shenzhen-Hong Kong Joint Funding Project (2nd principal contributor), he conducts research and development on privacy-preserving federated large-model systems. He has filed more than 50 invention patents, among which 19 have been granted. These patents cover core technical areas such as lightweight compression for large models, efficient processing of multimodal perception data, edge-cloud inference acceleration, and perception-reasoning optimization for embodied intelligence. Multiple patented technologies have been verified through industrial deployment in real-world business scenarios including smart finance and intelligent interaction. From the academic perspective, he has published over 10 papers at well-known international conferences including ICCV, ICASSP, INTERSPEECH, ACML and IJCNN.