Keming Yu

Keming Yu is a Professor and Chair in Statistics at the Department of Mathematics, Brunel University London, within the College of Engineering, Design and Physical Sciences. He is also the Impact Champion for REF in Mathematical Sciences. He joined Brunel in 2005 after holding positions at the University of Plymouth, Lancaster University, and The Open University. He earned his PhD from The Open University and earlier degrees in Mathematics and Statistics from Chinese institutions.

• PhD in Statistics – The Open University, UK
• MSc in Statistics – China
• BSc in Mathematics – China

His research centers on quantile regression, Bayesian modeling, survival analysis, and statistical methods for big data. His work spans applications in health, finance, environment, and social sciences. He has made significant contributions to robust and flexible regression methods, including expectile, mode, and censored quantile regression.

His recent publications (2023–2025) show a strong focus on streaming data, spatiotemporal modeling, high-dimensional data, and Bayesian methods. He frequently publishes in top-tier journals such as the Journal of the Royal Statistical Society Series A, B, and C, Statistica Sinica, and Computational Statistics and Data Analysis. His work often involves collaboration with international researchers, especially in China and Europe.

He has contributed to methodological discussions in leading statistical journals, demonstrating active engagement with the academic community. His work on financial risk, environmental statistics, and health data analysis reflects interdisciplinary impact.

• Reviewed and contributed to discussions on safe testing, confidence sequences, and betting-based inference.
• Active in developing methods for nonignorable missing data, censored models, and functional covariates.

He supervises PhD students and is involved in teaching and curriculum development, including as Course Director for the MSc Statistics with Data Analytics. His research is supported by extensive publication output and academic service.

He leads or contributes to research on Bayesian models, robust regression, and scalable methods for big data, often involving collaborations in interdisciplinary teams. His lab or research group focuses on statistical methodology development with real-world applications.