Qing (Wendy) Wang

Associate Professor of Mathematics

Statistician specializing in topics of U-statistics, nonparametric kernel density estimation, risk estimation, variance estimation, cross-validation, resampling schemes, and extrapolation/interpolation methods.

I was born and raised in Beijing, China. I came to the U.S. to pursue a graduate degree in 2007, and received a Ph.D. in Statistics from Penn State University in 2012 under the supervision of the late Dr. Bruce G. Lindsay. Prior to joining Wellesley, I was a tenure-track Assistant Professor of Statistics at Williams College from 2012 to 2015, and at Bentley University from 2015 to 2016.

My research focus on topics in U-statistics, risk estimation, variance estimation, and cross-validation. Some of my recent research interests also include penalization methods, model assessment tools, mediation analysis and jackknife empirical likelihood. My teaching considers both applied and theoretical topics in statistics, including introductory statistics, regression analysis, probability, multivariate data analysis, nonparametric statistics, and Bayesian statistics. I greatly enjoy working with students on research topics in statistics. I also have great passion in undergraduate statistics education, and I am interested in incorporating active learning and creating interactive and hands-on educational tools for teaching statistics.

I am one of the advisors for the new data science major at Wellesley. To learn more about the data science major requirements, please visit this website. (Note that effective Fall 2023, data science is no longer a structured individual major, but a regular major. Please refer to the data science website for the current DS major requirements.)

In my spare time, I enjoy baking, cooking, practicing yoga, traveling with my husband, and spending time with my two young children, Iwa and Ian.


  • B.S., Beijing Normal University
  • M.S., Pennsylvania State University
  • Ph.D., Pennsylvania State University

Current and upcoming courses

  • This is an applied regression analysis course that involves hands-on data analysis. Topics covered during the semester include simple and multiple linear regression models, model diagnostics and remedial measures, matrix representation of linear regression models, model comparison and selection, generalized linear regression models (e.g. binary logistic regression, multinomial logistic regression, ordinal logistic regression, and Poisson regression), and basic time-series autoregressive AR(p) models. Statistical language R will be used throughout the course to realize fitting linear (or generalized linear) regressions models, model diagnostics, model comparison and selection, and simulations.