Academic Profile

Dr. XIA obtained his PhD degree from the Chinese Academy of Sciences in Jan 2013. He was a visiting scholar at the Department of Mathematics, Michigan State University from Dec 2009- Dec 2012. From Jan 2013 to May 2016, he worked as a visiting assistant professor at Michigan State University. He joined Nanyang Technological University in Jun 2016.
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Asst Prof Xia Kelin
Assistant Professor, School of Physical & Mathematical Sciences - Division of Mathematical Sciences

The last century has witnessed the tremendous advancement of Biological Sciences. The availability of massive biological data, high-performance computers, efficient computational algorithms, and mathematical and physical models have paved the way for Biological Sciences to undertake a historic transition from being qualitative, phenomenological, and descriptive to being quantitative, analytical, and predictive. Under this transition, modern Mathematical Biology will be fundamentally changed from macroscale modelings (of species, population, disease, blood fluid, etc) to molecular based analysis (of protein, DNA, gene, virus, etc).

Dr. XIA's group focuses on Molecular Based Mathematical Biology (MBMB). The essential idea is to use computational tools from PDE, differential geometry, algebraic topology and statistical learning to study the biomolecular structure, flexibility, dynamics, and functions. His recent interests are topological data analysis (TDA), topology based machine learning/deep learning models, and their applications in drug design.
  • Artificial intelligence for the prediction of alternative splicing from epigenomics and transcriptomics data in cancer

  • Bipartite-Graph-Based Machine Learning Models for Biomolecular Interaction Analysis

  • Elucidation of Dynamic Opening and Closing Mechanism of Giant Protein Cage "Vault"

  • Geometric And Topological Modeling Of Biomolecular Structure, Dynamics And Function

  • Improving Photocatalysts That Recycle Microplastics to Fuels by Artificial Intelligence

  • Incorporating Quantum Chemistry Descriptors and Topological Features to Study G-quadruplex-stabilizer Complexes by Machine Learning Models

  • Machine Learning Models Based On Weighted Persistent-Homology for Structure-Based Drug Design

  • Persistent Homology Based Topological Approaches For Biomolecular Data Analysis

  • Topology-Based Featurization for Machine Learning Models in Materials Informatics
  • Kelin Xia. (2018). Persistent homology analysis of ion aggregation and hydrogen-bonding network. Physical Chemistry Chemical Physics, 20, 13448-13460.

  • Kelin Xia and Guo-Wei Wei. (2014). Persistent homology analysis of protein structure, flexibility and folding. International Journal for Numerical Methods in Biomedical Engineering, 30(8), 814-844.

  • Guo-Wei Wei, Qiong Zheng, Zhan Chen and Kelin Xia. (2012). Variational multiscale models for charge transport. SIAM Review, 54(4), 699-754.