Lanfeng Pan

PhD in Statistics

I am currently PhD candidate in Statistics at Iowa State University under the direction of Dr. Yehua Li. My research interests include High Performance Computing, False Discovery Rate Control, Clustering and Missing Data Analysis.

I completed my Master and Bachelor degree in Renmin University of China. My advisor of Master degree is Dr. Xiaoling Lu. My research was about data mining and matrix factorization at that time.

The programming languages I use most are R and Julia. I have been using R for 8 years and Julia for 4 years. I use R for plotting and reporting as well as small projects. When need to do heavy computing, I will turn to Julia for higher performance.

View CV in PDF.

Contact Me





Predicted item returning probability given customer and item information in an online shopping problem, utilizing ensemble algorithm consisting of C5.0, support vector machine and random forests. In charge of the C5.0 which gave the best performance.

Work experience

Model the effects of kidney transplant centers on surgery recipients survival time. Do clustering and heterogeneity detection on latent transplant centers effects while controlling the false discovery rate.

Project 1: Built shiny based user interface for data analysis and visualization on remote server. Project 2: Modeled the labor investment of pharmaceutical projects in decades to predict future labor investments. Also built an interactive visualization app for this data.


Research Interests

Software Packages

Implement kernel density estimation and kernel regression. In particular this package can deal with bounded kernel estimation using beta and gamma kernel and can choose bandwidth via cross valuation.

Fit a Generalized Linear Mixed Model with Gaussian mixture random effects and decide the number of components for Gaussian mixture. And further conduct a multiple test to detect heterogeneity while controlling the False Discovery Rate.

Implement a lot of useful and handy R functions in Julia. The purpose is to provide better statistical functions for Julia language as well as make it easy to translate R code into Julia.

Implement the Kasahara-Shimotsu Test to decide number of components in Gaussian Mixture Model.

R package to solve the nonnegative matrix factorization problem using coordinate descent.

Port the Yeppp! library into Julia, significantly speeding up several basic arithmetic operations.