Teaching

I enjoy teaching statistics at both the undergraduate and graduate levels and introducing students to modern statistical methodology through a combination of theory, computation, and real-world applications. In addition to classroom teaching, I regularly supervise independent reading courses and mentor undergraduate research projects.

University of Toronto

Regular Courses

• 2025–2026 — STA 2201: Methods of Applied Statistics II
  Graduate course introducing modern statistical learning methods, with emphasis on dimension reduction, clustering, multivariate analysis, and latent variable models.

• 2025 — STA 437: Methods for Multivariate Data Analysis
  Undergraduate and graduate course covering fundamental methods in multivariate statistics, including principal component analysis, canonical correlation analysis, factor analysis, independent component analysis, clustering, and graphical models.

• 2023 — STA 220: Practice in Statistics I Undergraduate course emphasizing data exploration, statistical inference, visualization, and communication using R.

• 2023 — STA 198: Probabilities Everywhere
  First-year seminar introducing probabilistic thinking through examples drawn from everyday life.

Undergraduate Research Training

• 2025–2026 — University of Toronto Statistical Sciences Research Program (UTSSRP)
  Co-organizer of the undergraduate summer research program. Taught the short course Curse of Dimensionality and mentored undergraduate research projects on single-cell genomics.

Reading Courses

• 2026 — Stability of Canonical Correlation Analysis
  Undergraduate reading course investigating the statistical behavior of canonical correlation analysis under a variety of high-dimensional settings.
  Paper

• 2026 — Privacy-Preserving Canonical Correlation Analysis
  Undergraduate reading course on statistical learning under privacy constraints, with a focus on performing canonical correlation analysis using the CKKS homomorphic encryption scheme.

• 2024–2025 — Inference and Clustering Analysis of Contact Matrices
  Undergraduate reading course on statistical methods for analyzing single-cell Hi-C data, covering matrix and tensor methods for clustering, inference, and chromatin reconstruction.
  Paper

• 2022 — Statistical Learning: Methods and Applications
  Reading course on modern statistical learning with applications to COVID-19 forecasting and predictive modeling.

Stanford University

• 2021 — STATS 32: Introduction to R for Undergraduates
  Undergraduate course introducing statistical programming, data visualization, and reproducible data analysis in R.

Moscow State University

• 2017 — Practicum in Statistics

• 2016 — Probability Theory