Reinforcement Learning

Prior Learning (Transfer Learning, Meta-Learning)

Optimization for Foundation Models

Robust Feedback Control

Data-Driven Classic Control (MPC)

Deep Learning


Forecasting and Control for Sustainability

Electric Power Train Design (McGill DBT)

Renewable Energy Management Systems

Intelligent Manufacturing

Artificial Pancreas

Intelligent Transportation (Autonomous Driving)



General introduction for research at McGill IALab. The Intelligent Automation Lab focusses on research and development of automation and machine learning solutions to enable novel environmentally sustainable systems. IALab also applies the same approach to enhance biomedical technologies. IALab develops innovative, robust data-driven control systems for environmentally sustainable engineered systems such as: electric vehicles (EV), autonomous vehicles (AV) renewable energy management systems (EMS), building heating, ventilation and air conditioning (HVAC) systems using renewable energy; biomedical applications such as the artificial pancreas; and polymer forming processes. The unifying theme of the research program is the application of feedback control, optimization and machine learning techniques which have the interesting property of modifying the dynamics of the systems, providing benefits such as low-energy consumption and robust high-performance operation.