Dr. FENG Long (冯龙博士) PhD(Rutgers University)



Assistant Professor


Contact Information Office: LAU-16-226
Phone: (+852) 3442-4037
Email: longfeng@cityu.edu.hk
Web: Personal Homepage


Long Feng obtained his PhD in Statistics from Rutgers University in 2017 and BSc in Statistics and Mathematics from Renmin University of China in 2012. Before Joining CityU, he was a Postdoctoral Associate at Yale University. His main research interests is on statistical machine learning, with an emphasis on image data analysis and variable selection.


Publications Show All Publications Show Prominent Publications
Journal Bi, Xuan. , Feng, Long. , Li, Cai. & Zhang, Heping. (in press). Modeling Pregnancy Outcomes through Sequentially Nested Regression Models. Journal of American Statistical Association.
Feng, Long. , Bi, Xuan. & Zhang, Heping. (2020). Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation. Journal of American Statistical Association. doi:10.1080/01621459.2020.1766468
Bi, Xuan. , Feng, Long. , Wang, Shiying. , Lin, Zijie. , Li, Tengfei. , Zhao, Bingxin. , Zhu, Hongtu. & Zhang, Heping. (2019). Common genetic variants have associations with human cortical brain regions and risk of schizophrenia. Genetic Epidemiology. 43/5. 548- 558. doi:10.1002/gepi.22203
Feng, Long. & Zhang, Cun-Hui. (2019). Sorted Concave Penalized Regression. Annals of Statistics. 47/6. 3069- 3098. doi:10.1214/18-AOS1759
Feng, Long. & Dicker, Lee. H. (2018). Approximate nonparametric maximum likelihood for mixture models: A convex optimization approach to fitting arbitrary multivariate mixing distributions. Computational Statistics & Data Analysis. 122. 80- 91. doi:10.1016/j.csda.2018.01.006Get


Conference Paper Feng, Long. , Ma, Ruijun. & Dicker, Lee. H. (2017). Nonparametric Maximum Likelihood Approximate Message Passing. 51st Annual Conference on Information Sciences and Systems (CISS). (pp. 1- 6). doi:10.1109/CISS.2017.7926084




Openings I am actively looking for PhD students interested in statistical machine learning, image data analysis and high-dimensional statistics. Candidates with strong mathematical or computing skills are welcome to apply.



Last update date : 10 Nov 2021