Dr. LIAN Heng (练恒博士) BSc (USTC) PhD(Brown)



Associate Professor


Contact Information Office: Y6538 Academic 1
Phone: +852 3442-6418
Fax: +852 3442-0250
Email: henglian@cityu.edu.hk
Web: Personal Homepage

Research Interests high-dimensional data analysis
Machine learning
functional data analysis
Bayesian statistics



Dr. Lian got his PhD in Applied Mathematics from Brown University in 2007. He joined the city university of HK in 2016, after 7 years as an assistant professor in Nanyang Technological University and then 2 years as a senior lecturer at the university of New South Wales. His research interest include high-dimensional data analysis, distributed statistical estimation for large data, Bayesian statistics and functional data analysis.
I am interested in taking PhD students to work in the area of statistics, machine learning, and optimization, with Bachalor's or Master's degree from a reputable university. Prospective students please directly contact me with transcript and standard English test results. I am also looking for Joint PhD student from mainland institutions that have established the Joint/Collaborative PhD program with cityU.
Current PhD students:
Yue Wang: 2020--2024
Jiamin Liu (joint PhD): 2020--2022
Wenqi Lu (joint PhD): 2019--2022
Previous PhD students:
Zengyan Fan (Lecturer at singapore university of social sciences), Zhaoping Hong (foxconn technology group, Taipei), Yuao Hu (Google Singapore), Xingyu Tang (Lectuerer at Singapore Polytechnic), Ye Tian (Gekko Artificial Intelligence Limited, Shenzhen/Hong Kong), Kaifeng Zhao (DBS singapore)

Publications
Di Wang, Yao Zheng, Heng Lian and Guodong Li, High-dimensional vector autoregressive time series modeling via tensor decomposition, Journal of the American Statistical Association, accepted
Yuankun Zhang, Heng Lian, Yan Yu, Ultra-high dimensional single-index quantile regression, Journal of Machine Learning Research, 21:1-25,2020
Heng Lian, Kaifeng Zhao and Shaogao Lv, Projected spline estimation of the nonparametric function in high-dimensional partially linear models for massive data, Annals of Statistics, 47, 2922-2949, 2019
Heng Lian and Zengyan Fan, Divide-and-conquer for debiased l1-norm support vector machine in ultra-high dimensions, Journal of Machine Learning Research, 18:1-26, 2018
Shaogao Lv, Huazhen Lin, Heng Lian and Jian Huang, Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space, Annals of Statistics, 46(2):781-813, 2018
Kejun He, Heng Lian, Shujie Ma and Jianhua Huang, Dimensionality reduction and variable selection in multivariate varying-coefficient models with a large number of covariates, Journal of the American Statistical Association, 113:746-754, 2018
Heng Lian, Hua Liang and Raymond. J. Carroll, Variance Function Partially Linear Single-Index Models, Journal of the Royal Statistical Society, Series B, 77(1): 171-194, 2015
Yuao Hu, Ye Tian and Heng Lian, Letter to editor (comments on the paper "Sparse least trimmed squares regression for analyzing high-dimensional large data sets" by Alfons et.al), Annals of Applied Statistics, 7(2): 1244-1246, 2013
Heng Lian, Semiparametric estimation of additive quantile regression models by two-fold penalty, Journal of Business and Economic Statistics, 30(3): 337-350, 2012
Robert Gramacy and Heng Lian, Gaussian process single-index models as emulators for computer experiments, Technometrics, 54(1): 30-41, 2012
Aditya Chopra and Heng Lian, Total variation, adaptive total variation and nonconvex smoothly clipped absolute deviation penalty for denoising blocky images, Pattern Recognition, 43(8):2609-2619, 2010
Heng Lian, Bayesian nonlinear principal component analysis using random fields, IEEE Trans. Pattern Analysis and Machine Intelligence, 31(4):749-754, 2009
Heng Lian, MOST: Detecting cancer differential gene expression, Biostatistics , 9(3): 411-418, 2008
Heng Lian, William Thompson, Robert Thurman, John Stam, William Noble and Charles.E. Lawrence, Automated mapping of chromatin structure in ENCODE, Bioinformatics, 24: 1911-1916, 2008
Heng Lian, On the consistency of Bayesian function approximation using step functions. Neural Computation 19(11):2871-2880, 2007


Previous Experience 2014 - 2016, Senior Lecturer, University of New South Wales.
2007 - 2014, Assistant Professor, Nanyang Technological University.



Editorial Services Associate Editor, Statistics and Its Interface
Associate Editor, Journal of the Korean Statistical Society



Last update date : 01 Dec 2021