Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Pengsheng Ji

Blurred image of the arch used as background for stylistic purposes.
Associate Professor
Associate Head

Education

  • PhD, Statistics, Cornell University
  • MS, Statistics, Nankai University
  • BS, Mathematics, Nankai University

Honors and Awards

  • M. G. Michael Award, University of Georgia, 2016
  • Annals of Applied Statistics (AOAS) Discussion Paper, 2016
  • Teaching Academy Fellow, University of Georgia, 2014
  • Provost Summer Research Grant, University of Georgia, 2014

Research Interests

  • Social Networks
  • Machine Learning
  • Big Data Analytics
  • Bibliometrics
  • Bioinformatics
  • Variable/Feature Selection

Selected Publications

  1. Ji, P., Jin, J., Ke, Z. T. and Li, W. (2021). Co-citation and co-authorship networks of statisticians  (with discussions). Journal of Business & Economic Statistics. Full text. 
  2. Wang, Z., Liang, Y. and Ji, P. (2020). Spectral algorithms for community detection in directed networks. Journal of Machine Learning Research (JMLR). Full text.
  3. Jiang, X., Ji, P. and Li, S. (2019). CensNet: Convolution with edge-node switching in graph neural networks.  International Joint Conference on Artificial Intelligence (IJCAI)Full text.
  4. Ji, P. and Nussbaum, M. (2017). Sharp minimax adaptation over Sobolev ellipsoids in nonparametric testing.  Electronic Journal of Statistics, Vol. 11, No. 2, 4515-4562. Full text.
  5. Ji, P. and Jin, J. (2016). Coauthorship and citation networks for statisticians (with discussions). Annals of Applied statistics,  Vol. 10, No. 4, 1779-1812. Presented in the AOAS Invited Lecture at the Joint Statistical Meetings (JSM) 2016 in Chicago.  Download the main paper, discussions (#1#2#3#4#5#6) and rejoinder.  Download data set and computer code.
  6. Ji, P. and Zhao, Z. (2014). Rate optimal multiple testing procedure in high dimensional regression. Full text at arXiv.
  7. Ji, P. and Jin, J. (2012). UPS delivers optimal phase diagram in high dimensional variable selection. Annals of Statistics, Vol. 40, No. 1, 73-103. Full text, Matlab code, and the R package ScreenClean.

Students

  1. Yu Wang, PhD student.
  2. Duna Zhan, PhD student.
  3. Xiaodong Jiang, PhD, 2019. Now Research Data Scientist at Facebook.
Research Areas:
Events featuring Pengsheng Ji

Consider a linear model Y = X + z, z N(0; In). Here, X = Xn;p, where both p and n are large, but p > n. We model the rows of X as i.i.d. samples from N(0; 1 n ), where is a pp correlation matrix, which is unknown to us but is presumably sparse. The vector is also unknown but has relatively few nonzero coordinates, and we are interested in identifying these…

We collect the coauthor and citation data for all research papers published in four of the top journals in statistics between 2003 and 2012, analyze the data from several different perspectives (e.g., patterns, trends, community structures) and present an array of interesting findings. (1) Both the average numbers of papers per author published in these journals…

Articles Featuring Pengsheng Ji
Monday, March 5, 2018 - 4:32pm

The Department of Statistics congratulates Dan Hall, Abhyuday Mandal, Cheolwoo Park, and Wenxuan Zhong on their recent promotion to the rank of Full Professor and Pengsheng Ji on his promotion to the rank of Associate Professor with tenure. This is an…

Tuesday, March 7, 2017 - 7:58am

Dr. Pengsheng Ji Published a Discussion Paper on Statistician Coauthorship and Citation Networks in the Annals of Applied Statistics.

Tuesday, January 12, 2016 - 4:23pm

Congrats to Pengsheng for his recognition of excellence in research! 

We are so proud of you! 

My Graduate Students

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.