Dr Xin Guo
Senior Lecturer in Mathematical Data Science
School of Mathematics and Physics
+61 7 334 69728
Priestley Building (67), Room 447
Researcher biography
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.
Journal Articles
Gu, Jiaxin, Chen, Minheng, Yuan, Yue, Guo, Xin, Zhou, Tian-yi and Fu, Qiang (2025). Drink like a man? Modified Poisson analysis of adolescent binge drinking in the US, 1976–2022. Social Science and Medicine, 364 117553, 1-9. doi: 10.1016/j.socscimed.2024.117553
Guo, Xin and Fu, Qiang (2024). The design and optimality of survey counts: a unified framework via the Fisher Information Maximizer. Sociological Methods and Research, 53 (3), 1319-1349. doi: 10.1177/00491241221113877
Wang, Chendi, Guo, Xin and Wu, Qiang (2024). Learning with centered reproducing kernels. Analysis and Applications, 22 (03), 507-534. doi: 10.1142/S0219530523400018
Gu, Jiaxin, Guo, Xin, Liu, Xiaoxi, Yuan, Yue, Zhu, Yushu, Chen, Minheng, Zhou, Tian-Yi and Fu, Qiang (2023). Gone with the weed: incidents of adolescent marijuana use in the United States, 1976-2021. Annals of Epidemiology, 88, 23-29. doi: 10.1016/j.annepidem.2023.10.002
Guo, Xin, Guo, Zheng-Chu and Shi, Lei (2023). Capacity dependent analysis for functional online learning algorithms. Applied and Computational Harmonic Analysis, 67 101567, 1-30. doi: 10.1016/j.acha.2023.06.002
Guo, Xin, Lin, Junhong and Zhou, Ding-Xuan (2022). Rates of convergence of randomized Kaczmarz algorithms in Hilbert spaces. Applied and Computational Harmonic Analysis, 61, 288-318. doi: 10.1016/j.acha.2022.07.005
Chen, Xiaming, Tang, Bohao, Fan, Jun and Guo, Xin (2022). Online gradient descent algorithms for functional data learning. Journal of Complexity, 70 101635, 101635. doi: 10.1016/j.jco.2021.101635
Fu, Qiang, Zhuang, Yufan, Zhu, Yushu and Guo, Xin (2022). Sleeping lion or sick man? Machine learning approaches to deciphering heterogeneous images of Chinese in North America. Annals of the American Association of Geographers, 112 (7), 2045-2063. doi: 10.1080/24694452.2022.2042180
Yuan, Yue, Gu, Jiaxin, Guo, Xin, Zhu, Yushu and Fu, Qiang (2022). Detecting temporal anomalies with pseudo age groups: homeownership in Canada, 1981 to 2016. Population, Space and Place, 28 (1) e2532, 1-18. doi: 10.1002/psp.2532
Fu, Qiang, Zhou, Tian-Yi and Guo, Xin (2021). Modified Poisson regression analysis of grouped and right-censored counts. Journal of the Royal Statistical Society. Series A: Statistics in Society, 184 (4), 1347-1367. doi: 10.1111/rssa.12678
Gu, Jiaxin, Guo, Xin, Veenstra, Gerry, Zhu, Yushu and Fu, Qiang (2021). Adolescent marijuana use in the United States and structural breaks: an age-period-cohort analysis, 1991–2018. American Journal of Epidemiology, 190 (6), 1056-1063. doi: 10.1093/aje/kwaa269
Fu, Qiang, Zhuang, Yufan, Gu, Jiaxin, Zhu, Yushu and Guo, Xin (2021). Agreeing to disagree: choosing among eight topic-modeling methods. Big Data Research, 23 100173, 100173. doi: 10.1016/j.bdr.2020.100173
Fu, Qiang, Guo, Xin, Jeon, Sun Young, Reither, Eric N., Zang, Emma and Land, Kenneth C. (2021). The uses and abuses of an age-period-cohort method: on the linear algebra and statistical properties of intrinsic and related estimators. Mathematical Foundations of Computing, 4 (1), 45-59. doi: 10.3934/mfc.2021001
Guo, Xin and Shi, Lei (2020). Preface of the special issue on analysis in data science: methods and applications. Mathematical Foundations of Computing, 3 (4), i-ii. doi: 10.3934/mfc.2020026
Guo, Xin, Li, Lexin and Wu, Qiang (2020). Modeling interactive components by coordinate kernel polynomial models. Mathematical Foundations of Computing, 3 (4), 263-277. doi: 10.3934/mfc.2020010
Fu, Qiang, Guo, Xin and Land, Kenneth C. (2020). Optimizing count responses in surveys: a machine-learning approach. Sociological Methods and Research, 49 (3), 637-671. doi: 10.1177/0049124117747302
Guo, Xin, Fu, Qiang, Wang, Yue and Land, Kenneth C. (2020). A numerical method to compute Fisher information for a special case of heterogeneous negative binomial regression. Communications on Pure and Applied Analysis, 19 (8), 4179-4189. doi: 10.3934/cpaa.2020187
Guo, Xin, Hu, Ting and Wu, Qiang (2020). Distributed minimum error entropy algorithms. Journal of Machine Learning Research, 21, 1-31.
Qin, Huihui and Guo, Xin (2019). Semi-supervised learning with summary statistics. Analysis and Applications, 17 (5), 837-851. doi: 10.1142/S0219530519400037
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
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.
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
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
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
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.
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
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
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
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
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
Conference Papers
Westerhout, Jacob, Nguyen, TrungTin, Guo, Xin and Nguyen, Hien Duy (2024). On the asymptotic distribution of the minimum empirical risk. 41st International Conference on Machine Learning, Vienna, Austria, 21-27 July 2024. Maastrict, The Netherlands: ML Research Press.
Fu, Qiang, Zhuang, Yufan, Gu, Jiaxin, Zhu, Yushu, Qin, Huihui and Guo, Xin (2019). Search for K: assessing five topic-modeling approaches to 120,000 Canadian articles. 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA United States, 9-12 December 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/BigData47090.2019.9006160