Dr. Boyuan Bai is a dedicated researcher currently pursuing his Ph.D. at the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China, specializing in multi-view scene reconstruction, deep reinforcement learning, and computer graphics. His innovative work includes the development of UncertainGS, an uncertainty-aware indoor reconstruction pipeline leveraging Gaussian Splatting to produce high-fidelity indoor surface models, as published in Neurocomputing (2025). Through this research, he introduced an uncertainty-aware learner utilizing cross-modal random network prediction to estimate prior cue uncertainties and integrated the Manhattan-world assumption to improve reconstruction in textureless environments, ensuring consistent geometric alignment of Gaussian kernels. His contributions reflect strong expertise in intelligent visual computing and advancing next-generation 3D reconstruction methodologies.