I am a specialist in machine learning, Bayesian statistics, and robotics. I am the founder of NQB, an artificial intelligence consulting firm that helps businesses solve complex problems through custom AI solutions. I work directly with clients to oversee the definition and implementation of their AI-driven solutions, ensuring that technical development remains aligned with their business objectives. I also lead NQB's product development, where we are building a VOIP platform powered by agentic AI voice bots on our own PBX infrastructure — engineered for low-latency audio, real-time conversational intelligence, and seamless integration with enterprise telephony. Years of fundamental research have given me a deep mathematical foundation that I bring to every problem I tackle — the ability to see beyond standard approaches, formulate original models, and design algorithms tailored to challenges that off-the-shelf methods simply cannot solve. My research has been published at major conferences including UAI, AAAI, and NeurIPS, and has been widely cited by the scientific community. I have delivered technologies that are now in operation across industries, from industrial inspection and manufacturing to life sciences and food processing, for organizations such as Medicago, Eddyfi, Thales, Optel, and Frontmatec.
Whether the input is a spectrum, an image, a waveform, or a point cloud, I approach every problem the same way: understand the data, formulate the mathematics, and build an algorithm that solves it.
Leading an AI development firm focused on applying machine learning technologies to solve industrial problems. Business development, technical direction, and delivering cutting-edge AI solutions to clients across various industries.
Conducting research in Bayesian machine learning and neural networks. Supervising graduate students in machine learning research projects.
Business development, marketing and administration of an AI development firm. Applying machine learning technologies to solve industrial problems.
Led research projects and supervised students in machine learning applications. Developed corporate training solutions and advanced data analytics pipelines.
Developed calibration systems for hyperspectral cameras and laser profilometers. Built classification models for hyperspectral imaging and multi-object tracking algorithms.
Maintained and developed Yelp ads software, analyzing big data for click-through rate prediction. Built a learning/testing pipeline service for ad targeting optimization.
Developed an advanced 3D vision solution using laser profilometry to extract critical landmarks on meat. Deep neural networks for semantic segmentation and detection enable precise robotic cutting paths.
Built a machine learning pipeline to predict structures in metal components using non-destructive measurement devices. State-of-the-art deep learning with Bayesian inference, deployed via ONNX.
Predicted plant yield from temporal metabolomics data measured with LCMS. Developed the Reference Concentration Network (RCN) and VectorizedLinear layer running millions of networks in parallel.
Created unique digital signatures from aluminum product images for reliable object re-identification. Robust matching pipeline effective under varying lighting and perspective conditions.
Forecasted temperature fluctuations in airplane avionic bays for Bombardier. Created a new Bayesian Variational Inference algorithm to estimate model uncertainty using RNN and GRU architectures.
Combined viral load data from wastewater with epidemiological metrics to detect COVID-19 waves in advance. Integrated time-series methods and deep learning with multi-head attention.
Conceived a $5.9M research program with 4 industrial partners (Bombardier, Thales, CAE, Bell Helicopter) across 5 universities focused on AI certification. Defined 4 research axes: Robustness, Interpretability, Confidentiality and Certification. Coordinated sub-teams and led the ML literature review.
Anticipated customer mortgage needs based on transaction history for Desjardins. Supervised a team of 4, selected architectures (TCN, LSTM, RNN), evaluated model effectiveness with LIFT metrics and delivered the solution on Azure.
Predicted student dropout probability per semester for Université Laval (VREX). Supervised a team of 2, designed the neural network architecture, evaluated model effectiveness and assisted in technology transfer. Built with Keras/TensorFlow and scikit-learn.
Developed a tracking algorithm for airplanes using RGB cameras at INO. Combined Gaussian processes for temporal smoothing to handle occlusion with a convolutional neural network for airplane detection. Responsible for implementation, integration and performance validation.
Predicted mushroom ripeness levels using hyperspectral imaging at INO. Developed data acquisition procedures, camera calibration, image segmentation and annotation systems, and a proprietary classifier for hyperspectral images. A patent was submitted for this technology.
Predicted water needs of basil plants using hyperspectral imaging at INO. Developed data acquisition, camera calibration, image segmentation and annotation systems, and a proprietary classifier for hyperspectral images.
Reconstructed 3D models of pipelines using a robotized laser profilometer at INO. Developed a proprietary Bayesian filter (Extended Kalman Filter) for inertial measurements, data imputation techniques for 3D laser data, and a learning algorithm for spatial-temporal positioning.
Developed unsupervised learning algorithms for detecting mental health conditions from proprietary signal data for diaMentis. Created a novel machine learning algorithm tailored to the project needs, implemented as a package for their proprietary ML platform.
Developed a complete neural network library in C++ for integration into a reinforcement learning-based adaptive cruise control at Université Laval. Built backpropagation, gradient descent, fully connected layers, activation functions and loss functions from scratch using Boost and Lapack.
Detected textures using a robotic finger with IMU signals at Université Laval. Developed a novel unsupervised learning algorithm (DPMoG) to identify textures without labels, and a Bayesian classifier to evaluate performance across 28 test textures.
Developed a Bayesian non-parametric model based on the Pitman-Yor process to predict letter and word sequences in text documents at Université Laval. The probabilistic predictor was used to compress documents, packaged as a substitute to rar/zip. Evaluated on the Calgary corpus.
Detected surfaces using an IMU mounted on a walking robot foot at Université Laval. Developed a novel unsupervised learning algorithm (PYPMoG) capable of identifying different surfaces without labels, and a Bayesian classifier to evaluate performance across 12 test surfaces.
Constructed a new probability distribution on infinite directed acyclic graphs at Université Laval. Derived mathematical models and Bayesian inference equations for Gibbs sampling and MCMC to learn the structure of neural networks and Bayesian networks. Supervised a research assistant.
Estimated relative positioning of two in-flight drones using mounted cameras at Université Laval. Implemented fiducial marker extraction, a Particle filter for position estimation, drone dynamics learning, and future position prediction. Built robotic remote control and communication with ROS.
Implemented data management and analytics services on AWS to target users with relevant ads. Developed a Bayesian network estimating click probability of users on ads. Integrated models in production, monitored performance, designed A/B tests and wrote unit tests.
Led the scientific design of a new CÉGEP program in artificial intelligence. Defined the skill set, course subjects and material, technologies to be taught, and anticipated the workforce needs of the machine learning industry.
I'm always interested in discussing AI research, machine learning projects, or potential collaborations. Feel free to reach out!
