Bio
Xiaoqing started her doctoral studies at The Alan Turing Institute in September 2019. She is also registered in the Department of Mathematics and Statistics at the University of Exeter.
Her PhD project primarily focused on theoretical and methodological innovation for highly multivariate high-dimensional spatial stochastic processes, with applications to quantifying global exposure to anthropogenic pollutants.
Her main contributions include developing a cross-Markov Random Field (cross-MRF) and a mixed spatial graphical model framework, which pushes the boundaries from different perspectives and addresses various challenges within a unified framework. These innovations demonstrate tangible advancements over existing methods in multiple benchmark evaluations.
This project is a joint work with Professor Peter Diggle and Professor James V. Zideck (FRSC, O.C.), theme-led by Professor Gavin Shaddick and administratively supported by Professor Mark Kelson.
Research interests
In general, Xiaoqing's research interests blend mathematical and statistical sciences, artificial intelligence, and natural sciences, primarily motivated by the challenges posed by ultra-complex data sets in real-world applications.
She is dedicated to developing innovative theories and methodologies that address real-world problems, with a particular focus on benefiting the public good, both in local communities and globally.
She is also interested in statistical education and was an experienced educator before pursuing her Master's degree.
Selected publications and papers
- Chen, Xiaoqing, Peter Diggle, James V. Zidek, Gavin Shaddick. (2024). “Highly Multivariate Large-scale Spatial Stochastic Processes – A Cross-Markov Random Field Approach”. arXiv:2408.10396
- Chen, Xiaoqing (2024). “Asymmetric Cross-Correlation in Multivariate Spatial Stochastic Processes: A Primer”. arXiv:2503.02903.
- Chen, Xiaoqing (2024). “Matern Correlation: A Panoramic Primer”. arXiv:2404.11427.
- Chen, Xiaoqing et al. (2023). “On the Stochasticity of Reanalysis Outputs of 4D-Var”. arXiv:2304.03648.