Rate My Professor Xin Guo

Xin Guo is a Senior Lecturer in Mathematical Data Science at the School of Mathematics and Physics, University of Queensland, a position he assumed in January 2022. He holds a BSc in Mathematics and Applied Mathematics from Beijing Normal University (2006), an MPhil (2008), and a PhD (2011) from City University of Hong Kong. Early in his career, he was a Research Fellow at City University of Hong Kong (October 2011 to February 2013) and a Postdoctoral Associate in the Department of Statistical Science at Duke University (February 2013 to August 2014). Prior to UQ, he worked at Hong Kong Polytechnic University.

His research specializes in statistical learning theory, including kernel methods, stochastic gradient methods, support vector machines, pairwise learning, online learning, error analysis, sparsity analysis, and algorithm implementation, as well as mathematical data science with applications to artificial intelligence, immunological bioinformatics, systems biology, and computational social science. Dr. Guo has obtained major grants such as the ARC Discovery Project DP23 (AUD$360,000, 2022) for Stochastic Majorization–Minimization Algorithms for Data Science, and RGC General Research Fund grants (HKD$598,015 in 2021, HKD$599,861 in 2020, HKD$335,927 in 2018, HKD$334,797 in 2017, HKD$488,501 in 2016, HKD$786,240 in 2015). Key publications encompass 'Kernel-based L2-boosting with structure constraints' (Journal of Machine Learning Research, 2025), 'Drink like a man? Modified Poisson analysis of adolescent binge drinking in the US, 1976–2022' (Social Science & Medicine, 2025), 'The design and optimality of survey counts: a unified framework via the Fisher Information Maximizer' (Sociological Methods and Research, 2024), 'On the asymptotic distribution of the minimum empirical risk' (ICML 2024), 'Gone with the weed: incidents of adolescent marijuana use in the United States, 1976–2021' (Annals of Epidemiology, 2023), and 'Capacity dependent analysis for functional online learning algorithms' (Applied and Computational Harmonic Analysis, 2023). These works highlight his contributions to machine learning theory and data analysis methodologies.