James Alexandre Goulet is a Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. His research focuses on Machine Learning Methods for Civil Engineering applications such as structural health monitoring (SHM) and infrastructure maintenance planning. He leads the Canari project for online change point detection in SHM and contributes to open-source libraries like cuTAGI for Bayesian neural networks. Affiliations : Chair in Machine Learning for Infrastructure Monitoring at Polytechnique Montréal, IVADO Institute member, and GRS (Structural Engineering Research Group) member Expertise : Building engineering, structural safety, applied probability, learning theories Recent research trends include Bayesian state-space models, LSTM neural network integration for infrastructure forecasting, and uncertainty quantification in SHM systems. His work emphasizes probabilistic methods and analytical inference over black-box approaches. Teaching includes courses on structural reliability and probabilistic data analysis for civil engineers. He supervises graduate students in topics ranging from damage detection algorithms to stochastic deterioration modeling of infrastructures.
