Dr Xin Guo

Senior Lecturer in Mathematical Dat

School of Mathematics and Physics

xin.guo@uq.edu.au

Priestley Building (67), Room 447

Researcher biography

Xin is a Senior Lecturer in Mathematical Data Science, at School of Mathematics and Physics, The University of Queensland. Xin obtained his BSc degree in Mathematics and Applied Mathematics, from Beijing Normal University in 2006. Xin obtained his MPhil and PhD degrees from City University of Hong Kong in 2008 and 2011 respectively, where he worked as a research fellow from Oct 2011 to Feb 2013. During Feb 2013 -- Aug 2014, Xin was a postdoctoral associate at Department of Statistical Science, Duke University. Before joining UQ in Jan 2022, Xin worked as research assistant professor and assistant professor at the Department of Applied Mathematics, The Hong Kong Polytechnic University. Xin's 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.

Featured projects	Duration
Deep Learning for Density Estimation

Publications

Journal Articles (26)

Conference Paper (1)

Journal Articles

Guo, Xin and Fu, Qiang (2022). The design and optimality of survey counts: a unified framework via the Fisher Information Maximizer. Sociological Methods & Research. doi: 10.1177/00491241221113877

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), 1-19. 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, 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

Guo, Xin and Shi, Lei (2020). Preface of the special issue on analysis in data science: M\methods and applications. Mathematical Foundations of Computing, 3 (4), i-ii. doi: 10.3934/mfc.2020026

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

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, 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, 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 Paper

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

Areas of research

Financial mathematics