Research Interests and Current Research

My research direction is to develop novel anomaly detection approaches in the internet of things. Substantial work has been done in this area; however, they are based on idealised settings. My work is to develop new statistical theories and methods to identify anomalies in IoT settings accurately and immediately.

Besides, I have a strong interest in applying statistical methods to solve real-world problems.


  • Paris-Saclay Change Detection Workshop (January 2023)
    • Oral: Online distributed changepoint detection methods for monitoring the Internet of Things
  • Research Student Conference in Probability and Statistics (September 2022)
    • Oral: An online distributed changepoint detection method for monitoring the Internet of Things
  • European Meeting of Statisticians (July 2023)
    • Oral: TBC


  • Steps to University for Mathematics Students (May 2023) – aimed at Year 12 girls
    • Presentation: Exploring the Fascinating World of Statistics (

Teaching Assistant


  • MATH104: Statistics
  • MSCI212: Statistical Methods for Business
  • MATH331: Bayesian Inference


  • MSCI212: Statistical Methods for Business
  • MSCI562: Intelligent Data Analysis and Visualisation
  • MSCI580: Analytics in Practice


  • Investigating the effects of temporal biased sampling and population temporal trend on a phylogeography method based on the structured coalescent

Statistical phylogeography is a statistical method which is based on the Full Bayesian method and Continuous Time Markov Chain, which could be used to find out the origin location and time, and the transmission route of the virus. Temporal biased sampling is introduced by inappropriate sampling method, and population temporal trend is a common phenomenon in evolution. By simulating four different scenarios, we evaluated the effect of MASCOT (which is a phylogeography method based on the Forward-Backward algorithm) under such situations. Thus, we draw a conclusion that MASCOT is not suitable to analyse genetic data having temporal biased sampling.