Qing (Wendy) Wang

Associate Professor of Mathematics

Contact
Phone:
+1 (781) 283-3397
Email:
qwang@wellesley.edu
Department
Mathematics
Data Science

Links

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.

Education

  • B.S., Beijing Normal University (北京师范大学)
  • M.S., Pennsylvania State University-Main Campus
  • Ph.D., Pennsylvania State University-Main Campus

Current and upcoming courses

Capstone in Data Science

DS340H

Senior data science majors enroll in this course in order to meet the major’s capstone requirement. The goal is to integrate and solidify the concepts learned in previous major courses. Students will demonstrate the ability to conduct applied projects via the steps in the data science process. Students will complete the capstone with the critical thinking needed to pose and refine questions that can be answered with data in an ethical way; the statistical skills needed to draw meaning from data appropriately; the computational skills needed to tackle practical data challenges; and the ability to collaborate, communicate, and critique in the context of modern data. The course is also a chance to practice and demonstrate key technical skills, such as code sharing on github or a strong command of data science libraries in both Python and R. At the end of the course, students will have created a project or portfolio that can be shared publicly. The course must be taken for a letter grade.
More Info

  • Multivariate Data Analysis

    STAT228

    This is a course in multivariate data analysis. Students will be introduced to modern multivariate techniques, their applications and interpretations, and will learn how to use these methods to understand relationships between variables, extract patterns, or identify clusters or classifications in a rich data set involving multiple variables. Topics covered during the semester include both dependence techniques (e.g. multiple linear regression, binary logistic regression, multinomial logistic regression, principal component analysis, linear discriminant analysis, decision trees, etc) and interdependence techniques (e.g. factor analysis, cluster analysis, etc). A selection of topics in machine learning and data mining are also introduced during the semester. Statistical language R is used in this class.