معرفی
Yanbo Tang is a Lecturer in Statistics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on statistical theory and methodology, including Bayesian inference, high-dimensional asymptotics, numerical integration, and applications in astrophysics and social sciences. He is affiliated with the Artificial Intelligence Network and Mathematics research and teaching staff at Imperial College.
- Education: Not explicitly stated in the provided text.
His research interests span statistical theory such as Laplace and saddlepoint approximations, adaptive quadrature methods, and copula models. He also explores applications in areas like extragalactic X-ray jet variability and parental psychological control effects on adolescents. Recent work emphasizes stochastic convergence rates and assumption-lean inference techniques.
Selected publications (2020–2024) highlight contributions to high-dimensional statistical problems, asymptotic behavior of likelihood methods, and Monte Carlo integration challenges. His work bridges theoretical advancements with practical computational solutions in complex data analysis.
No grants or awards are explicitly listed in the provided information. He contributes to academic discussions, such as commenting on Vansteelandt and Dukes' work on assumption-lean inference.
