visitor@petitengineer ~ %
B. A. Sc. EE & M.Eng. graduate in ECE (AI), University of Ottawa (2020 & 2026), building and evaluating machine learning systems, ranging from data pipelines and modeling to computer vision and robotics, all the while teaching and mentoring fellow engineers along the way.
Here are some of my projects! Full project write-ups are available! :)
I graduated in 2026 from the Master of Engineering program in Electrical and Computer Engineering at the University of Ottawa with a concentration in artificial intelligence. I exceeded the standard program requirements, which reinforced my passion for problems that combine hardware and computing with applied machine learning.
My interest in integrated hardware and software systems began in childhood with the Robotics Invention System 2.0 (Lego Mindstorms) and matured through multiple engineering degrees. Professionally, I split my time between building ML systems and teaching fundamentals to others.
As a consultant machine learning engineer with Synopsys (through the university), I developed an automated ML pipeline covering preprocessing, model deployment with TensorFlow Serving, webhooks, and a lightweight frontend. I also implemented evaluation workflows featuring charts, confusion matrices, and metrics such as F1, precision, and accuracy, and used MLflow to keep experiments and artifacts reproducible as teams iterated.
I competed on Quanser’s autonomous racing platform, finishing 8th out of 42 teams. That stack used ROS2 in Docker on an NVIDIA Jetson and combined OpenCV-based perception with time-triggered driving modes for lane keeping, traffic-light state estimation (including Hough-based methods), and classic vision operators for cones, stop signs, and road edges.
Earlier roles strengthened my software engineering discipline: at Fisheries and Oceans Canada I authored a Python library with unit tests to automate validation workflows, and on the University of Ottawa Bionics Team I led electrical development for a four degree of freedom hip-mounted exoskeleton, including a battery pack design.
I am most interested in building practical, affordable, and useful AI systems, such as autonomous platforms, reliable ML pipelines, and tools that make engineers faster and more effective. Outside the lab I enjoy teaching, tinkering with embedded hardware, and exploring how perception and control integrate on real platforms.
Technical skills: Python (scikit-learn, TensorFlow, JAX, PyTorch, XGBoost, pandas, OpenCV), C++, TensorFlow, Linux, Docker, ROS2, and Git. Additional experience: MATLAB, VHDL, Java, and embedded tooling.
