What began as a final course project for a group of Thompson Rivers University graduate students has become a published research paper in a leading international statistics journal.
Master of Science in Data Science students Hao Xu, Ayisha Cok, Caleb von Maydell and Max Li collaborated with associate professor Dr. Jabed Tomal to develop their course project into a manuscript published in Statistical Methods and Applications, a peer-reviewed journal published by Springer Nature.
The research began in winter 2024 as the final project for one of Tomal’s graduate data science courses. After the course ended, the students continued refining their work with Tomal, developing it into a scholarly article. As the work progressed, Dr. Nazrul Islam joined the research team as a collaborator.
“In this graduate course, I expected students not only to learn state-of-the-art methodologies but also to apply the knowledge they acquired to solve real-world problems,” said Tomal. “However, transforming a graduate course project into a publication in a prestigious peer-reviewed journal is an exceptionally ambitious feat, even for students at Ivy League universities. The students in our Master of Data Science program at TRU rose to this challenge.”
Following two rounds of rigorous peer review, the paper was accepted for publication in April and published in May, completing a process that took more than two years from classroom assignment to published research.
The achievement highlights how a student-led course project evolved into original scholarly research through faculty mentorship, giving the students first-hand experience with academic research and publication processes.
Xu, who graduated with a Master of Science in Data Science from TRU in 2025, said developing the work into a manuscript with Tomal was an invaluable experience.
“It was very rewarding to see a course project evolve into a published paper. I especially enjoyed implementing a textbook algorithm and applying it to real-world data, which made the research feel both practical and meaningful,” he said.
The paper examines Bayesian methods for modelling fertility rates, contributing to statistical research in an important area of demographic analysis.
Li, who also completed his master’s in data science last year, appreciated the opportunity to contribute to the development of a statistical methodology.
“The process highlighted how rigorous research is built through careful reasoning, open discussion and continuous refinement. It also showed how theoretical methods can be strengthened when they are examined closely and developed through collaboration,” he said.
Cok described the project as a rare chance to take research all the way from the classroom to publication.
“What stayed with me most was the revision process,” said Cok, who also graduated with her MSc in 2025. “Responding to reviewers and refining our methods taught me how much careful work separates a working analysis from a publishable result.”
“I really grew to appreciate the process of refining a method and feedback collaboratively,” added von Maydell. “My co-authors and reviewers gave great insight into bringing a project to publishable quality in both results and writing.”
Beyond providing a valuable learning experience, the research also contributed new knowledge to the field of Bayesian statistics.
“Through this publication, they made original and rigorous scientific contributions to methodological advances in Bayesian statistics and demonstrated the application of these advances to a complex public health problem,” said Tomal.
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