Uncovering Hidden Patterns in Complex Systems

Led by Prof Anastasia Papavasileiou from the Department of Statistics, University of Warwick and Prof Shan Luo from the Department of Statistics, Shanghai Jiao Tong University, this research project is tackling one of the most complex challenges in modern data science: understanding how interconnected systems evolve over time. These systems, such as biological networks or medical data, are vast, dynamic, and often messy, with information coming in many forms, from numbers to categories to rankings. Traditional methods struggle to make sense of this complexity, especially when the data doesn’t follow neat patterns.

This project is developing new statistical tools to reveal hidden connections and dynamic interactions within these systems. It focuses on three key questions: do certain factors influence how the network is structured, which elements are directly connected at any given moment, and how do past states shape what happens next? To answer these, the team is creating methods that can handle high-dimensional, mixed-type data and capture changes over time, even when the data is irregular or unpredictable.

The impact of this work goes far beyond theory. By uncovering patterns that were previously invisible, these tools can help scientists make better decisions and drive discoveries in critical areas such as cancer research and mental health. This collaboration between Warwick and Shanghai Jiao Tong University combines world-class expertise to advance statistical science and lay the foundation for future innovations in modelling complex networks.