Oakes Lab

The Oakes Lab at Polytechnique Montréal researches knowledge engineering for complex cyber-physical systems and digital twins.

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

kerian_fiter
Kérian Fiter
PhD Student · Since 2024
Systematic DT reporting & visualization
DTInsight paper
adil_lagrou
Adil Lagrou
PhD Student · Since 2026
DT interoperability
carlos_pambo
Carlos Pambo
PhD Student · Since 2024
Recommendations and patterns for accelerated DT construction
gabrielle_gallant
Gabrielle Gallant
Master's Student · Since 2025
Live model updating in DTs
Co-supervised with Oguzhan Tuysuz
angelica_portocarrero
Angelica Portocarrero
Master's Student · Since 2026
Previously MITACS Globalink Intern — 2025
LLMs for DT engineering
Interested in joining the lab? See the Join Us page for open opportunities and how to contact me.

Alumni Students/Interns

Name Role Years Topic
Mikkel Schmidt Andersen Research intern 2025 Probabilistic DT update scheduling
Louis Malassigne-Onfroy Research assistant 2024 Visualization of DT architectures — DTInsight paper
Amélie Bosphore Research assistant 2024 Implementation of modelled machine learning pipelines
Justin Caisse Research assistant 2023–2024 Ontological modelling of machine learning pipelines

Resources

At Polytechnique

Interesting reading and advice