This research project focuses on developing an automated system to search and analyze time-series tabular features in the financial institution’s machine learning pipeline. The goal is to identify relevant features and improve efficiency in the decision-making process. The project will begin by prototyping a system to support automated feature search patterns and researching feature search […]

The research project, guided by Professor Stephen Smith, focuses on addressing environmental challenges related to water pollution and debris detection in the water areas, with a specific emphasis on garbage and waste detection on the water surface. The project entails a systematic literature review and analysis of various computer vision models to detect and classify […]

Machine learning attempts to model high-level abstractions in data using multiple processing layers with complex structures or non-linear transformations. Federated learning is a distributed machine learning approach that allows multiple parties to collaborate on training while preserving user data privacy. However, the data from each party is typically non-independent and identically distributed (Non-IID), which can […]

The demand for eCommerce and online orders has risen rapidly in recent years, this drives the need for highly efficient and automated item sortation systems. Kindred AI is a technology company with the objective to bring artificial intelligence and robotic technologies into the workforce of eCommerce, parcel and order fulfillment. As a part of the […]

The proposed research aims at learning better representations for multivariate time series (MTS) data, which can be applied to various important real-life applications such as weather, traffic, and electricity forecasting. Better forecasting accuracies for these tasks could help with efficient risk aversion and decision making, and save costs for decision makers. The proposed research will […]

Ce projet traite le défi de la virtualisation du réseau électrique dans un contexte de transition énergétique caractérisé par une augmentation des ressources énergétiques décentralisées connectées, une numérisation des postes et automatismes et une convergence des technologies informationnelles avec les technologies opérationnelles. Le travail des stagiaires contribuera à mieux intégrer ces éléments dans les études […]

Mental illness is widespread, affecting half of the population during their lifetime. One solution to mitigate this is to detect the onset of mental illness early, as well as choose individualized treatment options based on biological markers. These biomarkers can predict behavioral and mental traits, like the risk to develop a mental illness. They can […]

Biomedical research, particularly preclinical research, is a complex and challenging field with a high failure rate of 98% in pharmaceutical research investment. Extracting relevant information from preclinical research papers involves synthesizing information from various sources, which is a demanding task that requires domain-specific knowledge. Natural language processing, specifically Large Language Models (LLMs), has demonstrated tremendous […]

Nova Scotia Power Inc. (NSPI) is the main provider of electricity in Nova Scotia. The largest disruptor to its system is the weather, which can lead to widespread equipment failure and outages across the grid. NSPI attempts to predict these damages before they occur to decide on the appropriate level of response and allocation of […]

Lateralization is one of the key principles of human brain organization. With a background in neuroimaging analysis of brain inter-hemispheric differences, the applicant Bin Wan will conduct his project in a Canadian research group which focuses on multimodal neuroimaging data. The intern will help with data acquisition of 7 Tesla MRI data in healthy individuals […]

Falls are a significant public health concern, particularly among older adults, who are more vulnerable to injury and death resulting from falls. According to the World Health Organization (WHO), falls are the second leading cause of accidental or unintentional injury deaths worldwide. With the global population aging rapidly, there is an urgent need to develop […]

The project involves enhancing and extending the in-house fluid flow solver using advanced mathematical and computing framework to be undertaken at the host university. The current in-house solver code can be efficiently applied to aerospace, marine and environmental flow problems. The collaboration with host university will result in a more advanced version of the code […]