About me

Hi, I’m Xin Xiong.

I am a Postdoctoral Research Fellow at Hong Kong Baptist University. I develop machine learning and statistical methods for multi-omics data analysis, with a focus on the tumor microenvironment, cancer systems biology, and immunology. My work aims to use computational models to uncover systems-level organization and interactions in complex biological tissues.

Research Focus

I work at the interface of computational biology, machine learning, and systems biology. I am interested in integrating high-dimensional multi-omics data to study cellular organization, tumor-immune interactions, gene regulation, and other questions where systems-level structure matters. Methodologically, I am especially interested in interpretable modeling, statistical learning, and tool development that helps bridge biological questions with quantitative analysis.

News

• Sep 06, 2026 - QClaw literature-tracking system: I am building an automated literature-tracking system based on QClaw, a variant of OpenClaw, to monitor recent advances from journals and arXiv across bioinformatics, cancer biology, and AI for science. The system updates daily through public APIs and email alert parsing. I welcome collaborators interested in improving this tracking system.

Selected Work

• DeSide: A unified deep learning framework for cellular deconvolution in the tumor microenvironment, designed to improve the interpretation of bulk transcriptomic data in cancer studies. Article, GitHub
• Ion mobility collision cross-section atlas: Contributed to a large-scale resource for known and unknown metabolite annotation in untargeted metabolomics, expanding computational support for molecular identification. Article
• MetCCS predictor: Developed a web server for predicting collision cross-section values of metabolites, helping support ion mobility-mass spectrometry based metabolomics analysis. Article

Brief Background

Before my current postdoctoral work, I completed my PhD in the Department of Physics at HKBU under the mentorship of Prof. Liang Tian, graduating in September 2025. During my doctoral training, I focused on machine learning and statistical approaches for multi-omics data analysis, with an emphasis on systems-level understanding rather than reductionist descriptions.

Earlier, I worked as a research assistant at the Shenzhen Institute of Synthetic Biology under the guidance of Prof. Xuefei Li, where I studied the tumor microenvironment and developed a cellular deconvolution method that later became DeSide. I also worked at ZhuLab within the Interdisciplinary Research Center on Biology and Chemistry in Shanghai, where I contributed to computational tools for metabolomics and mass spectrometry data analysis.

I received my bachelor’s degree (BEng) in Bioengineering from Xi’an Polytechnic University and my master’s degree (MEng) in Computer Science from Shanghai Jiao Tong University under the supervision of Prof. Hai Zhao.

Outside of research, I enjoy reading, watching movies, playing badminton, and practicing photography.