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Dr. FENG Long (馮龍博士)

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


  • 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


  • 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