The integration of machine learning models and the extraction of meaningful data into industrial workflows continues to accelerate, driving innovation and new business opportunities. Leveraging our expertise in mathematical and statistical modeling, we aim to apply these technologies to address critical challenges in agriculture, where up to 40% of crop yields are lost due to […]

Standing requires every claimant to establish a connection between his situation and the basis of the lawsuit which justifies his participation in the legal proceedings. It is often characterized as context sensitive, particularly in administrative law, given the diversity of decisions likely to be subject to judicial review of administrative action. The objective of my […]

Our research concerns a group of lipid trafficking proteins, scientifically known as ABC transporters, that only work when submerged in the sea of lipids, which we call membranes. These proteins can “pick up” lipid molecules, especially sterols, and move the lipids out or into the cells. We know that different members of these lipid trafficking […]

The project focuses on Variational Mean-Field Games: it will provide theoretical guarantees for the convergence of numerical algorithms currently used and possibly develop also novel machine learning methods for it.

This project address the so-called Quantum Marginal Problem from a mathematical, physics and computational perspective. It develops the mathematical theory of optimal transport adapt to quantum states in order to make fundamental advances in physical aspects and computational algorithms solving the quantum marginal problem.

When Kinaxis trains its machine learning models, it does so on two time scales. Every week or so, it looks at around 10 billion sales records and tries to learn the rapidly-changing “parameters” that best describe this data. Every 3-6 months, it updates 10 thousand collections of “hyperparameters” that govern the parameter-learning process. Kinaxis would […]

This research project aims to develop a comprehensive predictive framework for heat treatment furnaces to enhance operational efficiency, product quality, and environmental sustainability. The project focuses on creating a data preprocessing pipeline tailored for big machinery sensor data, innovative AI-enabled anomaly detection models, real-time furnace operation analysis, and quality prediction models. Key objectives include obtaining […]

Understand the performance of wood shearwalls with respect to heightened seismic loads especially for multi-storey wood structures. The proposed project is conducted in collaboration with the Canadian Wood Council as the industrial partner, and it aims to address the impact of extending the light frame wood shearwalls to taller buildings especially as it pertains to […]

This research project enables internship personnel, guided by their industry sponsors, to develop advanced software features for designing and operating vast solar power farms. Creating a digital twin of the system, these computational models apply the latest solar technologies to efficiently harvest light from the whole environment (sun, sky, and ground) under the complex shading […]

The proposed project studies the vibration behaviour of innovative mass timber floor systems, focusing on cross-laminated timber (CLT) floors supported by CLT beams and timber-concrete composite (TCC) floors with notched connections. Using laboratory testing and numerical modelling, the research aims to enhance the understanding of how these floors respond to dynamic loads, including the effects […]

This research investigates the fusion of multi-source geospatial data to overcome key scaling limitations in automated property assessment. The primary research problem addresses object occlusion in satellite imagery, where features can obscure ground-level surfaces and prevent accurate area measurement. We propose and evaluate a novel machine learning approach that geometrically fuses satellite imagery with ground-level […]

Nitrous oxide (N2O) is a powerful greenhouse gas with a global warming potential 298 times that of CO2, significantly contributing to climate change through agricultural activities, particularly the application of fertilizers. Accurate and scalable prediction of N2O emissions is essential for sustainable agriculture and climate mitigation. Current methods, such as LSTM-DLM neural networks proposed in […]