PIMS Distinguished Lecture: Tian Zheng (Online)

  • Date: 11/06/2020
  • Time: 13:30
Lecturer(s):

Tian Zheng, Department Chair of Statistics at Columbia University

 

Bio: Dr. Tian Zheng is Professor and Department Chair of Statistics at Columbia University. She obtained her PhD from Columbia in 2002. She develops novel methods for exploring and understanding patterns in complex data from different application domains such as biology, psychology, climatology, and etc. Her current projects are in the fields of statistical machine learning, spatiotemporal modeling and social network analysis. Professor Zheng’s research has been recognized by the 2008 Outstanding Statistical Application Award from the American Statistical Association (ASA), the Mitchell Prize from ISBA and a Google research award. She became a Fellow of American Statistical Association in 2014. Professor Zheng is the receipt of 2017 Columbia’s Presidential Award for Outstanding Teaching. In 2018, she will be the chair-elect for ASA’s section on Statistical Learning and Data Science. Professor Zheng was an associate editor for Journal of American Statistical Association - Applications and Case Studies from 2007 to 2013 and a current AE for Statistical analysis and data mining (SAM) and Statistics in Biosciences (SIBS), also a Faculty member of F1000 Prime. She is on the advisory board for STATS at Sense About Science America that targets to develop a statistical literate citizenry.
Location: 

Online

Topic: 

Adjusted Visibility Metric for Scientific Articles

Description: 

Measuring the impact of scientific articles is important for evaluating the research output of individual scientists, academic institutions, and journals. While citations are raw data for constructing impact measures, there exist biases and potential issues if factors affecting citation patterns are not properly accounted for. In this work, we address the problem of field variation and introduce an article-level metric useful for evaluating individual articles’ visibility. This measure derives from joint probabilistic modeling of the content in the articles and the citations among them using latent Dirichlet allocation (LDA) and the mixed membership stochastic blockmodel (MMSB). Our proposed model provides a visibility metric for individual articles adjusted for field variation in citation rates, a structural understanding of citation behavior in different fields, and article recommendations that take into account article visibility and citation patterns.

Other Information: 

This is an online event. Please register here

 

1:30 PM (PDT)