About the authors

Tiffany Timbers is an Associate Professor of Teaching in the Department of
Statistics and Co-Director for the Master of Data Science program (Vancouver
Option) at the University of British Columbia. In these roles she teaches and
develops curriculum around the responsible application of Data Science to solve
real-world problems. One of her favorite courses she teaches is a graduate
course on collaborative software development, which focuses on teaching how to
create R and Python packages using modern tools and workflows.

Trevor Campbell is an Associate Professor in the Department of Statistics at
the University of British Columbia. His research focuses on automated, scalable
Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and
Bayesian theory. He was previously a postdoctoral associate advised by Tamara
Broderick in the Computer Science and Artificial Intelligence Laboratory
(CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT, a Ph.D.
candidate under Jonathan How in the Laboratory for Information and Decision
Systems (LIDS) at MIT, and before that he was in the Engineering Science
program at the University of Toronto.

Melissa Lee is an Assistant Professor of Teaching in the Department of
Statistics at the University of British Columbia. She teaches and develops
curriculum for undergraduate statistics and data science courses. Her work
focuses on student-centered approaches to teaching, developing and assessing
open educational resources, and promoting equity, diversity, and inclusion
initiatives.