Teaching at Western University since 2023
Course: Introduction to Machine Learning, DS 3000
Training a model amounts to descending a loss surface. Here SGD, Momentum and Adam start from the same point; how and why their paths differ is a central lesson of his machine learning course.
See the courseCourse: Reinforcement Learning, AISE 4030
Q-learning is where his Reinforcement Learning course begins. The agent starts with no knowledge of the map, and its route shortens as it learns from each episode.
See all coursesCourse: Advanced Image Processing and Analysis, ECE 4438, ECE 9022 and ECE 9202
A 3 × 3 kernel slides across the image, and each output pixel is a weighted sum of the nine pixels beneath it. With different weights, the same sweep blurs, sharpens or embosses.
See all coursesCourse: Networking, ECE 4436 and ECE 9303
Each trace leaves London, Ontario, and crosses the world over optical fibre. The delay shown is the physical lower bound, distance divided by the speed of light in glass, and it is where his networking course begins.
See the courseCourse: Database Management Systems, SE 3309 and AISE 3309
A design begins as entities and relationships. Normalization removes the repetition a flat table would carry, the relations are declared in SQL, and a query joins them along their foreign keys.
See all coursesCourse: Software Project and Process Management, SE 3351
The Critical Path Method, solved live. A task with slack can slip without consequence; a delay on the critical path moves the finish of the whole project.
See all coursesStep 0. Loss: Adam 0.00, Momentum 0.00, SGD 0.00
Same surface, same start point, three optimizers.
Q-learning, live. Episode 0, last run ... steps, shortest possible ....
Sobel edge magnitude, kernel Gx and Gy, 3 × 3
Output 0% computed, one block of rows at a time
Tracing from London, Ontario.
Conceptual design: an entity-relationship model
Critical path: 7 tasks with zero slack. Finish in week 14.
Since 2023, he has taught thirteen course codes across eight subjects in the Software Engineering, AI Systems Engineering and Electrical and Computer Engineering programs. Introduction to Machine Learning and Networking have public course pages, with lecture slides for enrolled students.
Each ribbon runs term by term, from Fall 2023 to Fall 2026: taught teaching now
Four subjects
Teaching now
DS 3000 undergraduate
Teaching in Fall 2026
AISE 4030 undergraduate
Taught in Winter 2026
ECE 4438 undergraduate, ECE 9022 and ECE 9202 graduate
Taught in Winter 2025
New course
Graduate, M.Eng.
Designing, operating and evaluating governed agentic workflows: agents and subagents, reusable skills, tool integration through the Model Context Protocol, guardrails, and coordination across agentic platforms, applied across the engineering implementation lifecycle.
First offered in Winter 2027
Five subjects
Teaching now
ECE 4436 undergraduate, ECE 9303 graduate
Taught in Fall 2023, Fall 2024 and Fall 2025; teaching in Fall 2026
SE 4455 undergraduate, ECE 9016 graduate
Taught in Winter 2024, Winter 2025 and Winter 2026
SE 3309 and AISE 3309 undergraduate
Taught in Fall 2023 and Fall 2025
SE 3351 undergraduate
Taught in Fall 2024 and Fall 2025
SE 2202 undergraduate
Taught in Fall 2024
His teaching connects theory with industry practice in an inclusive, student-centred classroom. Lectures pair fundamental concepts with hands-on work in the tools of the field, and most courses culminate in a major team project that students defend in a live demonstration.
Class polls, real-time quizzes and collaborative problem solving keep every student involved, with immediate feedback in each lecture.
A decade of industry experience supplies real examples and lessons learned, and course content is updated regularly to reflect current technologies.
Neural networks are trained live in lecture, networking students analyze real traffic in Wireshark, and cloud computing students build on Google Cloud.
Teams deliver a working system in phases aligned with the lectures, and must be able to explain any line of it during a live demonstration.
Students learn to use AI tools effectively and responsibly, and remain accountable for everything they submit.
Support extends beyond the classroom, from guidance on projects and careers to recommendations for students pursuing graduate study.