Lizhi studied Applied Mathematics (Financial Engineering) in Shanghai. His last two years was spent at the University of Strathclyde, where he obtained a BSc in Mathematics and Statistics. He then completed a Master of Advanced Study (MASt) in Mathematical Statistics from Cambridge University. His essay for his MASt is about proving the error bound of Total Variant Denoising algorithm, which is an application of Lasso Regression in Computer Vision. Lizhi is interested in practical applications of statistics and the theories behind them. Currently, his main interest is in Machine Learning.

Research project title:
Complexity-based selection of large-scale network models

Supervisor(s):
Tiago Peixoto

Project description:
The large-scale structure of real-world network systems cannot be directly obtained by inspection, and require instead robust methods of description and extraction. One common approach is to identify modules or ”communities” via the statistical inference of generative models. Despite significant recent work in this direction, most existing methods rely on simplistic assumptions that disregard dynamical aspects of the network generation, and do not contain domain-specific information about the most likely mixing patterns. Lizhi is developing general tools applicable when the network grows over time (e.g. a citation network, or the world-wide-web), or when it contain heterogeneous assortative/disassortative mixing patterns (e.g. social networks).