Research Projects

My active research projects focus on: (i) statistical machine learning, (ii) Bayesian statistical inference, and (iii) statistical ecology.

Undergraduate Research Assistantship (UGRA)

Undergraduate students may have the opportunity to receive funding for summer research projects on topics of mutual interest. Senior-level undergraduate students (3rd year or above) with a background in statistics, data science, mathematics, or computer science are encouraged to discuss potential projects in person. Alternatively, students may choose from a selection of projects I have available. Experience in R and Python will be considered an asset. For productive discussions, please bring a short CV along with an unofficial copy of your academic transcripts.

Graduate Research Assistantship (GRA)

Prospective graduate students must meet the admission eligibility criteria for the Master of Science in Data Science or Master of Science, Environmental Science programs at TRU. Ideal candidates should have strong grades in upper-level undergraduate statistics/data science courses such as statistical inference, Bayesian statistics, regression, and machine learning. Research experience at the undergraduate level will be considered an asset. Priority will be given to students with experience in R, LaTeX, and scientific writing. While I may provide funding support for my graduate students, applicants are strongly encouraged to explore additional award/scholarship opportunities, including NSERC/CIHR CGS, BCGS, Graduate Student Research Mentorship, Ken Lepin, Dr. Sherman Jen, and the Environmental Science and Natural Resource Science Fellowship Award at TRU. Please note that these awards/scholarships have internal deadlines, so ensure that you apply well in advance. A small amount of funding may also be available as teaching assistantships for qualified applicants. When contacting me, please include your short CV, unofficial transcripts, and, for international students, your English language proficiency score to facilitate a productive discussion.