Oakes Lab

Objective: Accelerating Knowledge Engineering for Complex Systems

We start with the expert's domain knowledge, capture it in models, and then utilize it to better construct, optimize, verify, and validate complex systems.

Digital Twins Construction, reporting, and monitoring of DT systems for cyber-physical systems Systems Engineering Model-based systems engineering, MDE frameworks, and knowledge capturing for complex systems Machine Learning for Engineering Tasks ML and RL for fault injection, safety analysis, rationale mining, and model repair Knowledge Representation Ontologies, knowledge graphs, and semantic modelling to structure knowledge for complex systems

Lab Members

Kérian Fiter
PhD Student · Since 2024
DT federation, trust, and observability
DTInsight paper

Adil Lagrou
PhD Student · Since 2026
Formalization of DT component interoperability

Carlos Pambo
PhD Student · Since 2024
Recommendations and patterns for DT engineering

Gabrielle Gallant
Master's Student · Since 2025
AI-enhanced DT for vibration analysis in manufacturing
Co-supervised with Oguzhan Tuysuz

Angelica Portocarrero
Master's Student · Since 2026
Previously MITACS Globalink Intern — 2025
Structured DT engineering with LLMs

Fatima Aguel
MITACS Globalink Research Intern · Summer 2026
Ontological modelling of digital twin component repositories

Alumni Students/Interns

Resources

At Polytechnique

• New graduate student journey
• Requirements for the PhD degree
• Polytechnique Montréal’s Student Services (SEP): english / french
• Polytechnique student bourses/grants

Interesting reading and advice

• Behind the scenes: the struggle for each paper
• The largest problem in academia is discoverability: Be Findable
• How to write a thesis: Thesis Projects - A Guide for Students in Computer Science and Information Systems
• Fantastic advice on writing: Writing for Computer Science