Jun FanAssistant Professor
Since Fall 2017, I have been an Assistant Professor at Hong Kong Baptist University in the Department of Mathematics. My research interests include data science, statistical machine learning and applications in healthcare. Prior to starting at HKBU, I was working as a research associate at University of Wisconsin-Madison in the Department of Statistics mentored by Professor Ming Yuan.
Quantitative convergence analysis of kernel based large-margin unified machines (2020; with D.H. Xiang), Communications on Pure and Applied Analysis, accepted.
Optimal learning with Gaussians and correntropy loss (2020; with F.S. Lv), Analysis and Applications, to appear.
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, General Research Fund 12302819, 2020-2022.
Research Grants Council of Hong Kong, Early Career Scheme 22303518, 2018-2021.
National Natural Science Foundation of China, Young Scientists Fund 11801478, 2019-2021.
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)