Xin Guo 郭昕 Office: Priestley Building (67), Room 447 School of Mathematics and Physics The University of Queensland Brisbane, QLD 4072, Australia Email: xin.guo@uq.edu.au xinguo.math@gmail.com Tel: +61 7 3346 9728 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. My ORCID is 0000-0002-7465-9356, my Publon researcher id is M-6860-2017; my Scopus id is 56141420000; and my Google Scholar id is 32pRl-YAAAAJ.

Publications

• Chendi Wang, Xin Guo, and Qiang Wu, Learning with Centered Reproducing Kernels, [post-print: link], Analysis and Applications, online first, 2024.
• Jiaxin Gu, Xin Guo, Xiaoxi Liu, Yue Yuan, Yushu Zhu, Minheng Chen, Tian-Yi Zhou, and Qiang Fu, Gone with the weed: incidents of adolescent marijuana use in the United States, 1976-2021, [post-print: link], Annals of Epidemiology, Volume 88, Pages 23-29, December 2023.
• Xin Guo, Zheng-Chu Guo, and Lei Shi, Capacity dependent analysis for functional online learning algorithms, [post-print: link], Applied and Computational Harmonic Analysis, Volume 67(101567):1-30, November 2023.
• Yao Wang, Xin Guo, and Shao-Bo Lin, Kernel-based L2-boosting with structure constraints, submitted.
• Xin Guo, Junhong Lin, and Ding-Xuan Zhou, Rates of convergence of randomized Kaczmarz algorithms in Hilbert spaces, [post-print: link], Applied and Computational Harmonic Analysis, 61:288-318, November 2022.
• Xin Guo and Qiang Fu, The design and optimality of survey counts: a unified framework via the Fisher Information Maximizer, [post-print: link], Sociological Methods and Research, published online first, 2022.
• Qiang Fu, Yufan Zhuang, Yushu Zhu, and Xin Guo, Sleeping lion or sick man? Machine learning approaches to deciphering heterogeneous images of Chinese in North America, [post-print: link1,link2,link3], Annals of the American Association of Geographers, 112(7):2045-2063, 2022.
• Xiaming Chen, Bohao Tang, Jun Fan, and Xin Guo, Online gradient descent algorithms for functional data learning, [post-print: link1,link2], Journal of Complexity, 70(101635):1-14, June 2022.
• Yue Yuan, Jiaxin Gu, Xin Guo, Yushu Zhu, and Qiang Fu, Detecting temporal anomalies with pseudo age groups: homeownership in Canada, 1981 to 2016, [post-print: link1,link2], Population, Space and Place, 28(1,e2532):1-18, January 2022.
• Qiang Fu, Tian-Yi Zhou, and Xin Guo, Modified Poisson regression analysis of grouped and right-censored counts, [R package: GRCRegression], [post-print: link1,link2], Journal of the Royal Statistical Society, Series A, 184(4):1347-1367, October 2021.
• Jiaxin Gu, Xin Guo, Gerry Veenstra, Yushu Zhu, and Qiang Fu, Adolescent marijuana use in the United States and structural breaks: an age-period-cohort analysis, 1991--2018, [post-print: link1,link2], American Journal of Epidemiology, 190(6):1056-1063, June 2021.
• Qiang Fu, Xin Guo, Sun Young Jeon, Eric Reither, Emma Zang, and Kenneth Land, The uses and abuses of an age-period-cohort method: on the linear algebra and statistical properties of intrinsic and related estimators, [post-print: link1,link2], Mathematical Foundations of Computing, 4(1):45-59, February 2021.
• Qiang Fu, Yufan Zhuang, Jiaxin Gu, Yushu Zhu, and Xin Guo, Agreeing to disagree: choosing among eight topic-modeling methods, [post-print: link1,link2], Big Data Research, 23(100173):1-9, 15 February 2021.
• Xin Guo, Lexin Li, and Qiang Wu, Modeling interactive components by coordinate kernel polynomial models, [post-print: link1,link2], Mathematical Foundations of Computing, 3(4):263-277, November 2020.
• Qiang Fu, Xin Guo, and Kenneth C. Land, Optimizing count responses in surveys: a machine-learning approach, [R package: GRCdata], [post-print: link1,link2], Sociological Methods and Research, 49(3):637-671, August 2020.
• Xin Guo, Qiang Fu, Yue Wang, and Kenneth C. Land, A numerical method to compute Fisher information for a special case of heterogeneous negative binomial regression, [post-print: link1,link2], Communications on Pure and Applied Analysis, 19(8):4179-4189, August 2020.
• Xin Guo, Ting Hu, and Qiang Wu, Distributed minimum error entropy algorithms, [post-print: link], Journal of Machine Learning Research, 21(126):1-31, July 2020.
• Qiang Fu, Yufan Zhuang, Jiaxin Gu, Yushu Zhu, Huihui Qin, and Xin Guo, Search for K: assessing five topic-modeling approaches to 120,000 Canadian articles, [post-print: link1,link2], 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, pp. 3640-3647, 2019.
• Huihui Qin and Xin Guo, Semi-supervised learning with summary statistics, [post-print: link1,link2], Analysis and Applications, 17(05):837-851, 2019.
• Qiang Fu, Xin Guo, and Kenneth C. Land, A Poisson-multinomial mixture approach to grouped and right-censored counts, [post-print: link1,link2], Communications in Statistics - Theory and Methods, 47(2):427-447, 2018.
• Shao-Bo Lin, Xin Guo, and Ding-Xuan Zhou, Distributed learning with regularized least squares, [post-print: link], Journal of Machine Learning Research, 18(92):1-31, 2017.
• Zheng-Chu Guo, Dao-Hong Xiang, Xin Guo, and Ding-Xuan Zhou, Thresholded spectral algorithms for sparse approximations, [post-print: link], Analysis and Applications, 15(3):433-455, 2017.
• Kenneth C. Land, Qiang Fu, Xin Guo, Sun Young Jeon, Eric N. Reither, and Emma Zang, Playing with the rules and making misleading statements: a response to Luo, Hodges, Winship, and Powers, (invited paper), [post-print: link1,link2], American Journal of Sociology, 122(3):962-973, November 2016.
• Kevin A. McGoff, Xin Guo, Anastasia Deckard, Christina M. Kelliher, Adam R. Leman, Lauren J. Francey, John B. Hogenesch, Steven B. Haase, and John L. Harer, The Local Edge Machine: inference of dynamic models of gene regulation, [post-print: link], (software package: Local Edge Machine), Genome Biology, 17:214, 2016.
• Xin Guo, Jun Fan, and Ding-Xuan Zhou, Sparsity and error analysis of empirical feature-based regularization schemes, [post-print: link], Journal of Machine Learning Research, 17(89):1-34, 2016.
• Wen-Jun Shen, Hau-San Wong, Quan-Wu Xiao, Xin Guo, and Stephen Smale, Introduction to the peptide binding problem of computational immunology: new results, [post-print: link], Foundations of Computational Mathematics. 14(5):951-984. (The paper was first circulated with the title "Towards a mathematical foundation of immunology and amino acid chains" on arXiv https://arxiv.org/abs/1205.6031v2. All the simulation code is available upon request.) October, 2014.
• Wen-Jun Shen, Yu Ting Wei, Xin Guo, Stephen Smale, Hau-San Wong, and Shuai Cheng Li, MHC binding prediction with KernelRLSpan and its variations. Journal of Immunological Methods. 406:10-20, April 2014.
• Xin Guo and Ding-Xuan Zhou, An empirical feature-based learning algorithm producing sparse approximations, [post-print: link], Applied and Computational Harmonic Analysis. 32(3):389-400, 2012.
• Xin Guo, Learning gradients via an early stopping gradient descent method, [post-print: link], Journal of Approximation Theory. 162(11):1919-1944, November 2010.
• Lei Shi, Xin Guo, and Ding-Xuan Zhou, Hermite learning with gradient data. Journal of Computational and Applied Mathematics. 233(11):3046-3059, 1 April 2010.

Competitive Research Grants

• Stochastic Majorization--Minimization Algorithms for Data Science DP23, AUD$360,000, Nov 2022, ARC, Australia.
• Learning with artificial neural networks on spheres, GRF, HKD$598,015, Jun 2021, RGC, Hong Kong.
• Mathematical Analysis of Kernel-based Pairwise Learning and AUC Maximization, GRF, HKD$599,861, Jun 2020, RGC, Hong Kong.
• Analysis of kernel-based pairwise learning algorithms for large-scale data, GRF, HKD$335,927, Jun 2018, RGC, Hong Kong.
• Analysis of online learning algorithms with mini-batching and averaging, GRF, HKD$334,797, Jun 2017, RGC, Hong Kong.
• Analysis of the regularization of online learning algorithms, GRF, HKD$488,501, Jun 2016, RGC, Hong Kong.
• Mathematical analysis for coordinate kernel polynomial-based learning schemes that produce sparse approximations, ECS, HKD$786,240, Jun 2015, RGC, Hong Kong.

Teaching

• DATA7001, Introduction to Data Science, Falls of 2022--2024, and Springs of 2022--2024 (slides for Module 1 and Module 4 of Fall 2022, feedback/corrections highly appreciated)
• STAT4401, Advanced Statistics, Springs of 2022 and 2023
• MATH7501, Mathematics for Data Science 1, Fall 2023
• STAT1201, Analysis of Scientific Data, Fall 2024

[past teaching experiences in Hong Kong]

last updated: February 11, 2024