Xin Guo 郭昕
TU825, Yip Kit Chuen Building
The Hong Kong Polytechnic University
Hung Hom, Hong Kong
| Email: || email@example.com |
| Tel: || 3400 3751 |
| Fax: || 2362 9045 |
I am an Assistant Professor
of the Department of Applied Mathematics,
The Hong Kong Polytechnic University.
I got my PhD degree from City University of Hong Kong in 2011,
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
in Department of Statistical Science,
My research interests focus on learning theory
(kernel methods, stochastic gradient methods, support vector machine,
error analysis, sparsity analysis, and the implementation of algorithms),
systems biology, and computational social science.
Shaobo Lin, Xin Guo, and Ding-Xuan Zhou,
Distributed Learning with Regularized Least Squares,
Journal of Machine Learning Research, 18(92):1-31, 2017.
Qiang Fu, Xin Guo, and Kenneth C. Land,
Optimizing Count Responses in Surveys: A Machine-Learning Approach,
Sociological Methods & Research, accepted.
(R package: GRCdata)
Qiang Fu, Xin Guo, and Kenneth C. Land,
A Poisson-Multinomial Mixture Approach to Grouped and Right-Censored Counts
Communications in Statistics - Theory and Methods, online ready.
Zheng-Chu Guo, Dao-Hong Xiang, Xin Guo, and Ding-Xuan Zhou,
Thresholded Spectral Algorithms for Sparse Approximations,
Analysis and Applications, 15:3, 433-455, 2017.
Kenneth C. Land, Qiang Fu, Xin Guo, Sun Y. Jeon, Eric N. Reither, and Emma Zhang,
Playing with the Rules and Making Misleading Statements: a Response to
Luo, Hodges, Winship, and Powers,
(invited article), American Journal of Sociology, 122:3, 962-973, 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,
Genome Biology, 17:214, 2016.
Xin Guo, Jun Fan, and Ding-Xuan Zhou,
Sparsity and Error Analysis of Empirical Feature-Based
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.
Foundations of Computational Mathematics. October 2014, Volume 14, Issue 5, pp 951-984.
(The paper was first circulated with name "Towards a mathematical foundation of immunology and amino acid chains"
on arXiv http://arxiv.org/abs/1205.6031v2.
All the simulation code is available upon request.)
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. Volume 406, April 2014, Pages 10-20.
Xin Guo and Ding-Xuan Zhou,
An empirical feature-based learning algorithm producing sparse approximations.
Applied and Computational Harmonic Analysis.
Volume 32, Issue 3, May 2012, Pages 389-400.
Learning gradients via an early stopping gradient descent method.
Journal of Approximation Theory.
Volume 162, Issue 11, November 2010, Pages 1919-1944.
Lei Shi, Xin Guo, and Ding-Xuan Zhou,
Hermite learning with gradient data.
Journal of Computational and Applied Mathematics.
Volume 233, Issue 11, 1 April 2010, Pages 3046-3059.
AMA1110 Basic Mathematics I - Calculus and Probability & Statistics; AMA546 Statistical Data Mining
I was nominated as a candidate for the Best Teacher Award of the Department of Applied Mathematics in March 2017.
Analysis of online learning algorithms with mini-batching and averaging, GRF (Jan 2018 -- Dec 2020), RGC, Hong Kong.
Analysis of the regularization of online learning algorithms, GRF (Jan 2017 -- Dec 2019), RGC, Hong Kong.
Mathematical analysis for coordinate kernel polynomial-based learning schemes that produce sparse approximations, ECS (Oct 2015 -- Nov 2019), RGC, Hong Kong.
I code in C, R, Matlab, Python, Java, etc. (see here for a full list).
I code on GNU/Linux and Windows; on workstations and clusters.
I am a hobbyist programmer.