TEACHING & EDUCATION
Teaching machine learning.
I teach machine learning, deep learning, data science, and predictive analytics, connecting mathematical ideas to code and experiments.
University of Toronto · Course Instructor
2025–2026
- CSCB63H3 · Design and Analysis of Data Structures
Summer 2025
Algorithm analysis, heaps, balanced trees, hashing, disjoint sets, and graph structures.
- CSC236H1 · Introduction to the Theory of Computation
Fall 2025 · Summer 2026
Induction, correctness proofs, recurrences, and automata. Summer 2026 course website ↗
- GGR376H5 · Spatial Data Science II
Winter 2026
Spatial statistics, geospatial modelling, and data analysis with Python/R.
University of Toronto · Selected TA roles
- CSC311 · Introduction to Machine Learning
Lead TA · Winter 2024, 2025 & 2026
- CSC165H1 · Mathematical Expression and Reasoning for Computer Science
Head TA · Winter 2026
- CSC2516/CSC413 · Deep Learning & Neural Networks
Teaching Assistant · Winter 2022–2025 & Fall 2025
- STA414H1 · Statistical Methods for Machine Learning II
Teaching Assistant · Winter 2026
- CSC490 · Machine Learning for Vision
Teaching Assistant · Winter 2025
- ECE324H1 · Machine Intelligence, Software and Neural Networks
Teaching Assistant · Winter 2025
- APS360 · Applied Fundamentals of Deep Learning
Teaching Assistant · Winter 2024
- CHL5230HS · Applied Machine Learning for Health Data
Teaching Assistant · Fall 2023
Other teaching appointments
Seneca Polytechnic
Part-Time Professor · 2024–2025
Statistics for Analytics, Sentiment Analysis and Text Mining, Predictive Analytics, and Applied Data Mining and Modelling.
Northeastern University
Teaching Assistant · 2022–2024
Probability, statistics, Python analytics, predictive modelling, and databases.
York University & University of Waterloo
Teaching Assistant · 2015–2018
Engineering, numerical methods, and calculus.
Teaching in practice
Design and Analysis of Data Structures
CSCB63H3 · Course Instructor · June 23, 2025
Full lecture, University of Toronto Scarborough.
Linear Algebra
MATH185 · Teaching Assistant · March 22, 2019
Problem-solving tutorial, University of Toronto.
Teaching statement
I develop lectures, tutorials, assignments, and projects that move from intuition to mathematics to implementation. Live coding, debugging, and experimental comparison help students understand both how a method works and when it fails.
I build lessons around clear explanations, motivating examples, progressively harder problems, and opportunities for questions. Feedback helps students identify the step where their reasoning broke down and try again.
Read my teaching statement (PDF) ↗Advanced University Teaching Preparation Certificate, University of Toronto, 2025.