Education & Experience

Academic background, research, and industry roles.

Resume ↓ Academic CV ↓

Education

McMaster University — Hamilton, ON
M.Sc. in Statistics
September 2024 – October 2025
University of Toronto — Mississauga, ON
H.B.Sc., Applied Statistics Specialist and Mathematical Sciences Major
September 2019 – June 2024
  • CGPA: 3.92 / 4.00

Experience

McMaster University, Dept. of Mathematics & Statistics — Hamilton, ON
R Programmer (Research Assistant)
June 2025 – Present
  • Contributed to lme4 (43.4+ million downloads), a widely used R package for mixed-effects modelling.
  • Implemented feature improvements, resolved bugs, authored tests, and wrote documentation across the broader lme4 ecosystem including reformulas.
  • Serve as maintainer of mlmRev.
University of Toronto, Dept. of Mathematical and Computational Sciences — Mississauga, ON
Sessional Instructor
January 2026 – Present
  • Designed a course-culminating project requiring students to engineer production-style R packages and interactive R Shiny applications, simulating real-world data science development workflows.
  • Delivered a third-year computational statistics curriculum end-to-end, covering statistical computing concepts directly applicable to modern data science and machine learning pipelines.
University of Toronto, Dept. of Statistical Sciences — Toronto, ON
Statistical Web Application Developer (Research Assistant)
November 2022 – August 2024
  • Built and deployed a full-stack R Shiny web application for Bayesian diagnostic model comparison, enabling researchers to run ROC/AUC analysis and relative belief ratio inference across multiple models and sampling regimes without writing code.
  • View some of the work here.
Epson Canada — Markham, ON
Data and Evaluation Specialist Intern
May 2022 – August 2023
  • Delivered COGS forecasting models in Excel VBA that enabled rapid cost analysis of 3D computer vision solutions, directly supporting management decision-making.
  • Used Python and R to analyze feasibility data and build visualizations for 10+ computer vision business concepts (waste detection, robotic bin-picking), driving go/no-go decisions for the Business Development team.
  • Conducted market outreach to evaluate demand for 3D vision software and hardware, generating at least a 40-hour efficiency gain for the Business Development team.
University of Toronto, Dept. of Mathematical and Computational Sciences — Mississauga, ON
Teaching Assistant
July 2021 – August 2024
Prepared and ran tutorials for 200+ students, graded assessments for 1000+ students, hosted office hours, and independently facilitated large-scale final course projects for Sampling & Survey Design and Experimental Design.
  • MAT136H5 – Integral Calculus (Winter 2022)
  • STA107H5 – Introduction to Probability and Modelling (Winter 2022)
  • STA256H5 – Probability & Statistics I (Summer 2021, Fall 2021)
  • STA258H5 – Statistics with Applied Probability (Winter 2022)
  • STA260H5 – Probability & Statistics II (Winter 2022, Fall 2023, Summer 2024)
  • STA304H5 – Sampling & Survey Design (Fall 2022, Fall 2023)
  • STA305H5 – Experimental Design (Winter 2023, Winter 2024)
  • Math Circles – After-School Program for Secondary Students (Winter 2022, Fall 2023)
McMaster University, Dept. of Mathematics & Statistics — Hamilton, ON
Teaching Assistant
September 2024 – December 2025
  • STATS 2D03 – Introduction to Probability (Fall 2024, Fall 2025)
  • STATS 3A03 – Applied Regression Analysis with SAS (Fall 2024; grading)
  • STATS 3PG3 – Probability and Games of Chance (Fall 2025; grading)
  • MATH 1AA3 – Calculus For Science II (Winter 2025)
  • MATH 1ZB3 – Engineering Mathematics II (Winter 2025)
  • MATH 1XX3 – Calculus for Math and Stats II (Winter 2025; grading)
University of Toronto, Dept. of Mathematical and Computational Sciences — Mississauga, ON
Research Assistant (Multiple Contracts)
September 2020 – August 2024
  • Computer Science Education (Sep 2020 – Aug 2021) — Under Andrew Petersen: deployed surveys, created Python/R visualizations, and assisted in writing four published journal articles.
  • Computer Science Education (Apr 2023 – Aug 2024) — Under Tingting Zhu: quantitative analysis on video-based learning and YouTube analytics.
  • Statistical Education (Apr 2024 – Aug 2024) — Advised by Luai Al Labadi: quantitative analysis on the impact of incorporating projects into statistical courses.

Skills

Programming Languages: R, Python, SAS, HTML/CSS, SQL, Excel VBA

Machine Learning / Statistics: GLMs, logistic regression, linear mixed models, ensemble methods (random forest, boosting, bagging, BART), clustering (k-means, agglomerative, hierarchical), PCA, factor analysis

R Libraries: Shiny, Tidyverse (ggplot2, dplyr, tidyr, purrr, tibble), Easystats

Python Libraries: Matplotlib, NumPy, Pandas, SciPy, Seaborn, Plotly

Tools: Git, LaTeX, Qualtrics

Anna Ly · Personal Site · 2026