Xue Gong

Xue Gong

Presidential Postdoctoral Fellowship @ Nanyang Technological University, Singapore

About me

Welcome to my website!

I am a Presidential Postdoctoral Fellow at Nanyang Technological University, mentored by Kelin Xia. Before this, I received a PhD in applied mathematics, supervised by Desmond Higham and Kostas Zygalakis from the Maxwell Institute Graduate School, a joint graduate school between the University of Edinburgh and Heriot-Watt University. My PhD was supported by the MAC-MIGS CDT, a joint Centre for Doctoral Training program focused on mathematical analysis and computation.

My research interests include learning on graphs/hypergraphs such as embedding, clustering, and structure recovery, generative models that describe the interaction mechanism, and dynamics processes on graphs/hypergraphs. I am also interested in the application of graph/hypergraph-based approaches to real-world problems such as social network analysis, image classification, and natural language processing. My PhD research involves analyzing graphs/hypergraph embedding algorithms and deriving corresponding random graph models.

Before my PhD, I earned an MSc in Statistics from the National University of Singapore, and a Bachelor of Science in Physics from Nanyang Technological University in Singapore during which I developed my interest in understanding interactions and patterns in complex systems.

Here is my CV.

Research Interests

  • Spectral clustering
  • Graph models and algorithms
  • Topological data analysis
  • Application in biology, material science, and social sciences

Research Interests

I am interested in graph embedding/graph drawing algorithms that find the position of nodes to reveal certain structures and understand what generative model they are assuming. These models take the node position as input and generate edges randomly. These models have several potential benefits: they help us quantify structures and answer the questions such as “why should we draw the graph this way rather than another?” They also provide a model to generate synthetic data to produce desired structures. They also help interpret the graph embedding, for example, if we know the position of two nodes, we could learn the probability distribution of the edge between them using the model.

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