Jun FanAssociate Professor
I have been serving as an Assistant Professor in the Department of Mathematics at Hong Kong Baptist University since Fall 2017, focusing my research on statistical machine learning, deep learning theory, and their applications. Previously, I worked as a research associate under the mentorship of Professor Ming Yuan in the Department of Statistics at University of Wisconsin-Madison.
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2013 Ph.D. in Mathematics, City University of Hong Kong
Supervisor: Professor Ding-Xuan Zhou
2009 B.S. in Statistics, Beijing Normal University
I am looking for highly motivated PhD students and postdocs with solid mathematical or statistical skills, who are interested in statistical machine learning and related areas. Please send me your CV if you are interested.
Approximation of smooth functionals using deep ReLU networks (2023; with L. H. Song, Y. Liu and D. X. Zhou), Neural Networks, 166:424-436.
Approximation of nonlinear functionals using deep ReLU networks (2023; with L. H. Song, D. R. Chen and D. X. Zhou), Journal of Fourier Analysis and Applications, 29:50.
Online gradient descent algorithms for functional data learning (2022; with X. M. Chen, B. H. Tang and X. Guo), Journal of Complexity, 70:101635.
Comparison theorems on large-margin learning (2021; with A. Benabid and D.H. Xiang), International Journal of Wavelets, Multiresolution and Information Processing, 19(5):2150015.
Optimal learning with Gaussians and correntropy loss (2021; with F.S. Lv), Analysis and Applications, 19(1):107-124.
Quantitative convergence analysis of kernel based large-margin unified machines (2020; with D.H. Xiang), Communications on Pure and Applied Analysis, 19(8):4069-4083.
A statistical learning approach to modal regression (2020; with Y.L. Feng and J. Suykens), Journal of Machine Learning Research, 21(2):1-35.
Convergence analysis of distributed multi-penalty regularized pairwise learning (2020; with T. Hu and D.H. Xiang), Analysis and Applications, 18(1):109-127.
An RKHS approach to estimate individualized treatment rules based on functional predictors (2019; with F.S. Lv and L. Shi), Mathematical Foundations of Computing, 2(2):169-181.
Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age (2018; with S.I. Feld et al.), AMIA Jt Summits Transl Sci Proc., 81-90.
Quantifying predictive capability of electronic health records for the most harmful breast cancer (2018; with Y.R. Wu et al.), Proc SPIE Int Soc Opt Eng., 10577:105770J.
Learning rates for regularized least squares ranking algorithm (2017; with Y.L. Zhao and L. Shi), Analysis and Applications, 15(6):815-836.
Breast cancer risk prediction using electronic health records (2017; with Y.R. Wu et al.), IEEE International Conference on Healthcare Informatics (ICHI), 224-228.
Discriminatory power of common genetic variants in personalized breast cancer diagnosis (2016; with Y.R. Wu et al.), Proc SPIE Int Soc Opt Eng., 9787:978706.
Consistency analysis of an empirical minimum error entropy algorithm (2016; with T. Hu, Q. Wu and D.X. Zhou), Applied and Computational Harmonic Analysis, 41(1):164-189.
Structure-leveraged methods in breast cancer risk prediction (2016; with Y.R. Wu et al.), Journal of Machine Learning Research, 17(235):1-15.
Sparsity and error analysis of empirical feature-based regularization schemes (2016; with X. Guo and D.X. Zhou), Journal of Machine Learning Research, 17(89):1-34.
Comments on "Personalized dose finding using outcome weighted learning" (2016; with M. Yuan), Journal of the American Statistical Association, 111(516):1524-1525.
Comparing mammography abnormality features and genetic variants in the prediction of breast cancer in women recommended for breast biopsy (2016; with E. Burnside et al.), Academic Radiology, 23(1):62-69.
Regularization schemes for minimum error entropy principle (2015; with T. Hu, Q. Wu and D.X. Zhou), Analysis and Applications, 13(4):437-455.
Parameterized BLOSUM matrices for protein alignment (2015; with D.D. Song et al.), IEEE Transactions on Computational Biology and Bioinformatics, 12(3):686-694.
Learning theory approach to minimum error entropy criterion (2013; with T. Hu, Q. Wu and D.X. Zhou), Journal of Machine Learning Research, 14:377-397.
Research Grants Council of Hong Kong, GRF HKBU12302923, 2024-2026.
Research Grants Council of Hong Kong, GRF HKBU12303220, 2021-2023.
Research Grants Council of Hong Kong, GRF HKBU12302819, 2020-2022.
Research Grants Council of Hong Kong, GRF HKBU12301619 (transferred from Weiyang Ding), 2019-2022.
Research Grants Council of Hong Kong, ECS HKBU22303518, 2018-2021.
National Natural Science Foundation of China, Young Scientists Fund 11801478, 2019-2021.
MATH 4226 – Introduction to Deep Learning (Fall 2023)
ORBS 7030 – Business Statistics with Python (Fall 2022)
MATH 1005 – Calculus I (Spring 2022)
MATH 1025 – Introduction to Mathematics and Statistics (Fall 2021)
MATH 1005 – Calculus I (Fall 2020 & Spring 2021)
MATH 1025 – Introduction to Mathematics and Statistics (Fall 2019 & Spring 2020)
MATH 1025 – Introduction to Mathematics and Statistics (Fall 2018 & Spring 2019)
ORBS 7030 – Business Statistics and Database Management (Fall 2018)
MATH 1205 – Discrete Mathematics (Spring 2018)
ORBS 7030 – Business Statistics and Database Management (Fall 2017)