I'm a mathematics PhD at HIT, specializing in AI for pathology, AI for sensors, embodied intelligence, and lunar soil image analysis. I have published over 40 papers in top AI venues such as IEEE TIP, NeurIPS, AAAI, and ICCV (with one paper cited over 800 times) and have applied for 19 national invention patents. My work extends to biosignal analysis for epilepsy detection and fatigue monitoring, as well as lunar soil image processing in collaboration with HIT's lunar exploration team. Additionally, I have initiated and led academic activities, founding the "Hongli Cup" Mathematical Modeling Competition and the Machine Learning-Big Data Lecture Series, fostering a vibrant academic community. During my PhD, I have led projects funded by the National Natural Science Foundation of China and Guangdong's Science and Technology Innovation Strategy, and received honors such as the National Scholarship and HIT Outstanding Graduate. I serve as a reviewer for Information Fusion, Pattern Recognition, CVPR, ECCV, and AAAI, and as session chair for international conferences such as BIBE and ICRAIC. I am eager to attend the Hong Kong Laureate Forum to engage with Nobel laureates and distinguished scientists, gain insights into cutting-edge research, and explore interdisciplinary collaborations.

Harbin Institute of Technology

My research centers on advancing artificial intelligence based on mathematics to tackle challenges in multiple domains: AI for Sensors, computational pathology, and lunar soil image analysis. In AI for Sensors, I integrate physical laws into deep learning models to enhance low-cost inertial sensors, improving motion tracking and signal quality. This involves addressing issues like noise reduction and signal reconstruction, with practical applications in motion capture and industrial inspection. In computational pathology, I develop algorithms for virtual staining, tissue segmentation, and cancer subtype classification, enhancing diagnostic precision in whole-slide medical images. Additionally, I apply AI to lunar soil analysis, focusing on particle segmentation and topological morphology to support planetary science. My work employs deep learning techniques to bridge AI with physical sciences, aiming to elevate machine perception and enable scientific breakthroughs. Through this interdisciplinary approach, I contribute to innovative solutions that push the boundaries of technology and discovery across these fields.