Guibo Luo is an Assistant Professor and Ph.D. Supervisor at the School of Electronic and Computer Engineering, Peking University. He received his Ph.D. degree from Peking University, and subsequently conducted postdoctoral research at Harvard Medical School and Massachusetts General Hospital. His research focuses on privacy-preserving computation and foundation model training, with an emphasis on discovering and quantifying scientific patterns from heterogeneous real-world data while ensuring privacy and security in collaborative settings. Recently, he has been investigating efficient multi-institutional collaborative intelligence without exposing private data. To this end, he has developed a systematic research framework that connects (i) the creation of real-world multi-center datasets, distribution-shift characterization, and benchmark evaluations, (ii) communication-efficient privacy-preserving learning and secure computation paradigms, and (iii) privacy-preserving collaboration between foundation models and lightweight edge models under stringent communication and compute constraints. His work further emphasizes reliability and accountability in real deployments, and has led to practical deployments in healthcare, embedded systems, and embodied intelligence. He has published more than 80 papers in leading journals and conferences, including IEEE TPAMI, IEEE JBHI, Radiology: AI, IEEE TCSVT, IEEE TCDS, Journal of Digital Imaging, CVPR, ECCV, AAAI, KDD, ICSE, IJCAI, and MICCAI. He also serves as a reviewer for journals and conferences such as IEEE TIP, IEEE TMM, IEEE TNNLS, Knowledge-Based Systems, CVPR, ICCV, ICLR, NeurIPS, KDD, and MICCAI.