Yake Wei's research focuses primarily on multimodal perception and learning mechanisms. She has published more than ten papers in top-tier artificial intelligence journals and conferences, including TPAMI, NeurIPS, ICML, and CVPR, with multiple papers selected for oral presentation at NeurIPS and CVPR. Representative works include OGM, a balanced multimodal learning algorithm designed to improve the effective utilization of heterogeneous multimodal information, and MokA, a parameter-efficient fine-tuning method for multimodal large language models that builds on modality-specific characteristics. She was selected for the CVPR Doctoral Consortium and has received honors including the Baidu Scholarship and the Wu Yuzhang Scholarship of Renmin University of China. She also regularly serves as a reviewer for journals and conferences including TPAMI, NeurIPS, ICML, and CVPR.