I got my PhD in Applied Mathematics from Brown University in 2007. I joined the City University of HK in Dec 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. My research interest include high-dimensional data analysis, distributed statistical estimation for large data, Bayesian statistics and functional data analysis.
I have funding available to take 2-3 PhD students to start in 2025, in the area of statistics, machine learning, and optimization. General requirements are the following:
1. Undergraduate degree in math/stat with average mark>=85/100 from a 985 university (or equivalent). For C9 universities this threshold of 85 can be relaxed. Master degree is not necessary but a master degree from a good university is a plus.
2. English requirement: TOEFL>=79 or IELTS>=6.5 or CET 6>=490, unless your first degree was obtained from an institution where the language of teaching is English
Unless you are not currently a student, you should always use your official 'edu' email. Sorry I am not able to reply to all applicants. If you do not receive my reply within one week, please think of it as unsuccessful.
Current PhD students:
Yue Wang: 2020-
Jiahao Peng, 2022-
Zhihe Zhao, 2022-
Jiabao Wang, Joint PhD from Jilin U, 2022-
Junzhuo Gao, 2024-
Bei Zhang, Joint PhD from USTC, 2024-
Zhengyu Zhu, Joint PhD from ECNU, 2024-
Previous PhD students:
Zengyan Fan (Lecturer at singapore university of social sciences), Zhaoping Hong (foxconn technology group, Taipei), Yuao Hu (Google Singapore), Xingyu Tang (Lecturer at Singapore Polytechnic), Ye Tian (Gekko Artificial Intelligence Limited, Shenzhen/Hong Kong), Kaifeng Zhao (DBS singapore), Wenqi Lu (2019-2022, Joint PhD, currently at Nankai University), Jiamin Liu (2020-2023, Joint PhD, currently at University of Science and Technology Beijing)
Grants
GRF, 2024, 11300424
NSFC, 2023, 12371297
GRF, 2022, 11311822
GRF, 2021, 11300721
GRF, 2019, 11300519
GRF, 2018, 11301718
NSFC, 2018, 11871411
Publications
Xu Liu, Heng Lian and Jian Huang, More efficient estimation of multivariate additive models based on tensor decomposition and penalization, Journal of Machine Learning Research, 25:1-26, 2024
Yue Wang and Heng Lian, Sparse additive support vector machines in bounded variation space, Information and Inference: a Journal of the IMA, 13: iaae003, 2024
Wangli Xu, Jiamin Liu and Heng Lian, Distributed estimation of support vector machines for matrix data, IEEE Transactions on Neural Networks and Learning Systems, 35: 6643-6653, 2024
Tianhai Zu, Heng Lian, Brittany Green and Yan Yu, Ultra-high dimensional quantile regression for longitudinal data: an application to blood pressure analysis, Journal of the American Statistical Association, 118: 97-108, 2023
Shaobo Li, Yan Yu, Shaonan Tian, Xiaorui Zhu and Heng Lian, Corporate default probability: A discrete single-index hazard model approach, Journal of Business and Economic Statistics, 41: 1288-1299, 2023
Xiaoyu Zhang, Di Wang, Heng Lian and Guodong Li, Nonparametric quantile regression for homogeneity pursuit in panel data models, Journal of Business and Economic Statistics, 41: 1238-1250, 2023
Jiamin Liu, Wangli Xu, Fode Zhang and Heng Lian, Properties of standard and sketched kernel Fisher discriminant, IEEE Trans. Pattern Analysis and Machine Intelligence, 45: 10596-10602, 2023
Jiamin Liu and Heng Lian, On optimal learning with random features, IEEE Transactions on Neural Networks and Learning Systems, 34: 9536-9541, 2023
Heng Lian and Jiamin Liu, Decentralized learning over a network with Nystrom approximation using SGD, Applied and Computational Harmonic Analysis, 66: 373-387, 2023
Heng Lian, Distributed learning of conditional quantiles in the reproducing kernel Hilbert space, Neural Information Processing Systems (NeurIPS), 2022
Yingying Zhang, Yan-Yong Zhao and Heng Lian, Statistical rates of convergence for functional partially linear support vector machines for classification, Journal of Machine Learning Research, 23:1-24,2022
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, 117:1338-1356, 2022
Shaogao Lv and Heng Lian, Debiased distributed learning for sparse partial linear models in high dimensions, Journal of Machine Learning Research, 23:1-32,2022
Ke Yuan, Heng Lian and Wenyang Zhang, High dimensional dynamic covariance matrices with homogeneous structure, Journal of Business and Economic Statistics, 40:96-110, 2022
Heng Lian, Jiamin Liu and Zengyan Fan, : Distributed learning for sketched kernel regression, Neural Networks, 143:368-376,2021
Fode Zhang, Rui Li and Heng Lian, : Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection, Information Sciences, 547:244-254, 2021
Brittany Green, Heng Lian, Yan Yu and Tianhai Zu, Ultra-high dimensional semiparametric longitudinal data analysis, Biometrics, 77:903-913,2021
Heng Lian, Xinghao Qiao and Wenyang Zhang, Homogeneity pursuit in single index models based panel data analysis, Journal of Business and Economic Statistics, 39:386-401, 2021
Wenqi Lu, Zhongyi Zhu and Heng Lian, High-dimensional quantile tensor regression, Journal of Machine Learning Research, 21:1-30, 2020
Yuankun Zhang, Heng Lian, Yan Yu, Ultra-high dimensional single-index quantile regression, Journal of Machine Learning Research, 21:1-25,2020
Heng Lian, Fode Zhang and Wenqi Lu, Randomized sketches for kernel CCA, Neural Networks, 127:29-37, 2020
Weihua Zhao, Fode Zhang and Heng Lian, Debiasing and Distributed Estimation for High-dimensional Quantile Regression, IEEE Transactions on Neural Networks and Learning Systems, 31, 2569-2577, 2019
Fode Zhang and Heng Lian, Partially functional linear regression with quadratic regularization, Inverse Problems, 35:105002, 2019
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, 46:781-813, 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
Heng Lian, Peng Lai and Hua Liang, Partially linear structure selection in Cox models with varying coefficients, Biometrics, 69(2): 348-357, 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
Heng Lian, Xin Chen and Jian-Yi Yang, Identification of partially linear structure in additive models with an applications to gene expression prediction from sequences, Biometrics, 68(2): 437-445, 2012.
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
Submitted papers:
Randomized Tensor Decomposition and Optimization in the Tucker and Tensor Train Formats
Fast global convergence of decentralized lasso in high dimensions
Distributed approximate Newton-type method for functional regression
See also Google Scholar Google site
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 :
13 Nov 2024
