A machine learning system that turns messy HTML webpages into clean, structured event data faster, smarter, and more reliably than traditional web scraping.
Client: Emre Bengu & Philippe Sabourin
Technical Advisor: Arya Rahgozar
Adjunct Professor
The School of Engineering Design and Teaching Innovation, University of Ottawa
Yusuf Hilal
Project Manager & Developer
Christine Jue
System Integration Specialist
Anthony Nasr
Machine Learning Developer
The DTI 5902 course gave us the opportunity to develop Webstract as more than just a class project. Through this course, we were able to take an initial idea and turn it into a working machine learning prototype built around a real client need. The structure of the course encouraged us to move through the full design process, from concept development and technical experimentation to testing, iteration, and final presentation.
It also gave us valuable experience working as a team in a professional setting, where we had to balance client expectations, technical constraints, and project scope. Through this process, we strengthened our skills in machine learning, system design, communication, and collaboration, while building a solution that reflects both the academic and practical value of industry-focused engineering projects.