Beijing Normal University | China
Yu Xiao He is a lecturer at Beijing Normal University with a strong background in decision science and operations research. He received his B.S. degree in Communication Engineering from Sichuan University, Chengdu, China, in 2014, and earned his Ph.D. in Management Science from the National University of Defense Technology, China, in 2020. He worked at the National University of Defense Technology from 2020 to 2022 before joining Beijing Normal University. His research focuses on multi-criteria decision analysis (MCDA) and rank aggregation, as well as graph theory and voting theory, addressing challenges in ranking, consensus building, and decision support. To date, he has published 24 documents, which have collectively received 158 citations, resulting in an h-index of 8, reflecting his growing influence in his fields of expertise. His contributions combine rigorous theoretical development with practical applications, aiming to improve methods for aggregating rankings and supporting complex decision-making processes. Recognized for his interdisciplinary approach, he integrates engineering, management, and social-choice methodologies in his work. Overall, Yu Xiao He is a rising scholar whose research is advancing both the theory and practice of multi-criteria decision-making, ranking aggregation, and graph-based modeling, offering valuable insights for academics and practitioners alike.
Featured Publications
Xiao, Y., Wang, B., Deng, Y., & Wu, J. (2025). “From ratings to rankings: A complementary approach for student evaluations of teaching in higher education.” Studies in Educational Evaluation.
Zhu, H., Xiao, Y., Chen, D., & Wu, J. (2025). “A robust rating aggregation method based on temporal coupled bipartite network.” Information Processing & Management.
Li, G., Xiao, Y., & Wu, J. (2024). “Rank aggregation with limited information based on link prediction.” Information Processing & Management.
Xiao, Y., Zhu, H., Chen, D., Deng, Y., & Wu, J. (2023). “Measuring robustness in rank aggregation based on the error-effectiveness curve.” Information Processing & Management.
Xiao, Y., Deng, H.-Z., Lu, X., & Wu, J. (2021). “Graph-based rank aggregation method for high-dimensional and partial rankings.” Journal of the Operational Research Society.