Dr Xin Guo
Dr

Xin Guo

Email: 
Phone: 
+61 7 334 69728

Background

I am a Senior Lecturer in Mathematical Data Science, at School of Mathematics and Physics, The University of Queensland. I obtained my BSc degree in Mathematics and Applied Mathematics, from Beijing Normal University in 2006. I obtained my MPhil and PhD degrees from City University of Hong Kong in 2008 and 2011 respectively, where I was working as a research fellow from Oct 2011 to Feb 2013. During Feb 2013 -- Aug 2014, I was working as a postdoctoral associate at Department of Statistical Science, Duke University. Before joining UQ in Jan 2022, I worked at Hong Kong Polytechnic University. My research interests cover statistical learning theory (kernel methods, stochastic gradient methods, support vector machine, pairwise learning, online learning, error analysis, sparsity analysis, and the implementation of algorithms), mathematical data science, and their applications to artificial intelligence, immunological bioinformatics, systems biology, and computational social science.

Availability

Dr Xin Guo is:
Available for supervision

Fields of research

Applied mathematics Approximation theory and asymptotic methods Mathematical Sciences Numerical and computational mathematics Statistics

Qualifications

  • Doctor of Philosophy of Applied Mathematics, City University of Hong Kong

Research interests

  • machine learning theory

    kernel methods, stochastic gradient methods, support vector machine, pairwise learning, online learning, error analysis, sparsity analysis, and the implementation of algorithms

  • mathematical data science

  • computational social science

Search Professor Xin Guo’s works on UQ eSpace

32 works between 2010 and 2025
All (32) Journal Article (30) Conference Publication (2)

21 - 32 of 32 works

2019

Journal Article

Semi-supervised learning with summary statistics

Qin, Huihui and Guo, Xin (2019). Semi-supervised learning with summary statistics. Analysis and Applications, 17 (5), 837-851. doi: 10.1142/S0219530519400037

Semi-supervised learning with summary statistics

2018

Journal Article

A Poisson-multinomial mixture approach to grouped and right-censored counts

Fu, Qiang, Guo, Xin and Land, Kenneth C. (2018). A Poisson-multinomial mixture approach to grouped and right-censored counts. Communications in Statistics: Theory and Methods, 47 (2), 427-447. doi: 10.1080/03610926.2017.1303736

A Poisson-multinomial mixture approach to grouped and right-censored counts

2017

Journal Article

Distributed learning with regularized least squares

Lin, Shao-Bo, Guo, Xin and Zhou, Ding-Xuan (2017). Distributed learning with regularized least squares. Journal of Machine Learning Research, 18 92, 1-31.

Distributed learning with regularized least squares

2017

Journal Article

Thresholded spectral algorithms for sparse approximations

Guo, Zheng-Chu, Xiang, Dao-Hong, Guo, Xin and Zhou, DIng-Xuan (2017). Thresholded spectral algorithms for sparse approximations. Analysis and Applications, 15 (3), 433-455. doi: 10.1142/S0219530517500026

Thresholded spectral algorithms for sparse approximations

2016

Journal Article

Playing with the rules and making misleading statements: A response to Luo, Hodges, Winship, and powers

Land, Kenneth C., Fu, Qiang, Guo, Xin, Jeon, Sun Y., Reither, Eric N. and Zang, Emma (2016). Playing with the rules and making misleading statements: A response to Luo, Hodges, Winship, and powers. American Journal of Sociology, 122 (3), 962-973. doi: 10.1086/689853

Playing with the rules and making misleading statements: A response to Luo, Hodges, Winship, and powers

2016

Journal Article

The Local Edge Machine: inference of dynamic models of gene regulation

McGoff, Kevin A., Guo, Xin, Deckard, Anastasia, Kelliher, Christina M., Leman, Adam R., Francey, Lauren J., Hogenesch, John B., Haase, Steven B. and Harer, John L. (2016). The Local Edge Machine: inference of dynamic models of gene regulation. Genome Biology, 17 (1) 214, 214. doi: 10.1186/s13059-016-1076-z

The Local Edge Machine: inference of dynamic models of gene regulation

2016

Journal Article

Sparsity and error analysis of empirical feature-based regularization schemes

Guo, Xin, Fan, Jun and Zhou, Ding-Xuan (2016). Sparsity and error analysis of empirical feature-based regularization schemes. Journal of Machine Learning Research, 17, 1-34.

Sparsity and error analysis of empirical feature-based regularization schemes

2014

Journal Article

Introduction to the Peptide Binding Problem of Computational Immunology: New Results

Shen, Wen-Jun, Wong, Hau-San, Xiao, Quan-Wu, Guo, Xin and Smale, Stephen (2014). Introduction to the Peptide Binding Problem of Computational Immunology: New Results. Foundations of Computational Mathematics, 14 (5), 951-984. doi: 10.1007/s10208-013-9173-9

Introduction to the Peptide Binding Problem of Computational Immunology: New Results

2014

Journal Article

MHC binding prediction with KernelRLSpan and its variations

Shen, Wen-Jun, Wei, Yu Ting, Guo, Xin, Smale, Stephen, Wong, Hau-San and Li, Shuai Cheng (2014). MHC binding prediction with KernelRLSpan and its variations. Journal of Immunological Methods, 406, 10-20. doi: 10.1016/j.jim.2014.02.007

MHC binding prediction with KernelRLSpan and its variations

2012

Journal Article

An empirical feature-based learning algorithm producing sparse approximations

Guo, Xin and Zhou, Ding-Xuan (2012). An empirical feature-based learning algorithm producing sparse approximations. Applied and Computational Harmonic Analysis, 32 (3), 389-400. doi: 10.1016/j.acha.2011.07.005

An empirical feature-based learning algorithm producing sparse approximations

2010

Journal Article

Learning gradients via an early stopping gradient descent method

Guo, Xin (2010). Learning gradients via an early stopping gradient descent method. Journal of Approximation Theory, 162 (11), 1919-1944. doi: 10.1016/j.jat.2010.05.004

Learning gradients via an early stopping gradient descent method

2010

Journal Article

Hermite learning with gradient data

Shi, Lei, Guo, Xin and Zhou, Ding-Xuan (2010). Hermite learning with gradient data. Journal of Computational and Applied Mathematics, 233 (11), 3046-3059. doi: 10.1016/j.cam.2009.11.059

Hermite learning with gradient data

Current funding

  • 2023 - 2026
    Stochastic majorization--minimization algorithms for data science
    ARC Discovery Projects
    Open grant
  • 2023 - 2027
    Analytics for the Australian Grains Industry (AAGI)
    Grains Research & Development Corporation
    Open grant

Availability

Dr Xin Guo is:
Available for supervision

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Supervision history

Current supervision

  • Doctor Philosophy

    Pairwise learning with artificial neural networks

    Principal Advisor

  • Doctor Philosophy

    Investigating the properties of stochastic Majorization-Minimization algorithms and their variants

    Principal Advisor

Enquiries

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