معرفی
Yanbo Tang is a Lecturer (assistant professor) in the Department of Mathematics at Imperial College London, focusing on statistical inference and computational statistics. He earned his PhD (2022), MSc (2016), and BSc (2015) from the University of Toronto and Concordia University, respectively.
- PhD in Statistics, University of Toronto (2017-2022)
- MSc in Statistics, University of Toronto (2016)
- BSc in Actuarial Science, Concordia University (2015)
His research spans high-dimensional statistics, computational statistics, statistical genetics, and astrostatistics, with a focus on methods for handling nuisance parameters, adaptive quadrature in Bayesian inference, and approximation techniques like Laplace and saddlepoint methods. His work addresses challenges in reproducibility (P-value behavior) and practical applications in astronomy and genetics.
Recent publications highlight trends in high-dimensional statistical theory, Bayesian computational methods, and interdisciplinary applications such as analyzing parental control effects and astrophysical data variability. These works emphasize mathematical rigor and practical utility in complex models.
- Scientific Awards: NSERC CGS D (2020-2021), NSERC PGS D (2018-2020), Ontario Graduate Scholarship, ISBA Best Student/Postdoc Paper Award (2021), and multiple departmental teaching awards.
He supervises MSc and PhD students in statistics and statistical machine learning, emphasizing projects in high-dimensional inference and computational methods. His service roles include organizing workshops and admissions panels, with referee duties for journals like JRSSB and Statistical Sciences.
-medium.jpg)
