Machine Learning for Network Management and Control

The goal of this research project is to identify ways to apply machine learning technology to help communication network operators cope with the vast amounts of data they must process to understand the health of their networks and to quickly resolve problems.

Embedded sensor fusion network

Highly accurate 3D object detectors require significant computational resources, and reducing computation and memory load while maintaining the same level of performance is a critical task for any safe and reliable autonomous vehicle. This research project investigates the deployment of an accurate 3D object detection model to a resource constrained architecture by changing the model structure, its parameters as well as its activity during operation.

AI-Based Automated Methodologies for Supply Chains: High Precision Tabular Detection and Semantic Modeling of Electronic Components from Datasheets

This project will develop a hybrid framework by integrating AI and machine learning methods with tabular information extraction and semantic modeling to improve the state-of-the-art precision and recall in tabular detection while maximizing the value of extracted information for industrial applications.

Probabilistic Transitive Closure of Fuzzy Cognitive Maps: Algorithm Enhancement and Application to Work Integrated Learning

Shopify has a well-developed partnership with universities for a work-integrated Bachelor of Computer Science degree. It is in their best interest to see their student interns successfully transition to the work place. In the application part of this study, we will use a fuzzy cognitive map (that is, a special type of a graph) to represent expert knowledge on determinants of success and well-being of a student intern, Moreover, a relatively new mathematical model – transitive closure – will be applied to analyze this data and compute a set of guidelines for better learning outcomes.

Artificial Intelligence (AI) Powered Adaptive Flight Controller for Novel Unmanned Aerial Vehicle (UAV) with Commercial and Humanitarian Applications

The project entails research into machine learning techniques to control unmanned aerial vehicles (UAVs) with complex flight characteristics for surveillance and cargo transportation applications. In addition, it advances networked UAV fleet control and optimization methodologies to improve the potential of UAV fleets to perform coordinated tasks efficiently and reliably.

Building a National Ocean Literacy Strategy

This collaborative, pan-Canadian and consultation-based research project will develop an ocean literacy strategy for Canada with the aim of elevating Canadians understanding of the importance of ocean health and their capacity to participate in ways that promote a sustainable ocean ecosystem and economy. The interns will respectively coordinate the overall national consultation process (pdf#1), facilitate regional consultations (pdf #2; pdf #3; pdf #4; intern #5) and synthesize regional reports into a draft national strategy (pdf #1; intern #6).

Improved pulse pressure approximation and pattern recognition algorithm for prediction of blood pressure-related health issues

Continuous blood pressure (BP) monitor is highly beneficial for detection and prevention of stroke and cardiovascular disease. The most common BP monitor technique still relies on using a cuff that slows the blood flow, which is both uncomfortable and makes continuous monitoring impossible. Furthermore, research has shown that due to the numerous artifacts, the existing cuff-less BP monitoring technologies such as pulse transit time (PTT) and tonometry are not effective.

Calibration-Free Continuous Pulse Oximeter Monitoring Using Deep Learning

Oxygen saturation, i.e. SpO2 is the fifth most important vital sign after heart rate (HR), body temperature (BT), blood pressure (BP), and breathing rate (BR). Oxygen saturation indicates if sufficient oxygen is being supplied to the body.
Continuous SpO2 monitoring is highly beneficial for detection and prevention of several diseases.

Strengthening capacity of Canadian civil society organizations and their partners in the Global South to address gender inequality: Engagements, perceptions and uses of feminist approaches in international development

The goal of this project is to strengthen the capacity of Canadian civil society organizations (CSOs) working around the world to combat gender inequality. The Mitacs post-doctoral candidate, working closely with the Canadian Council of International Co-operation, a coalition of over 80 CSOs in Canada, will examine how different feminist approaches are applied in international development work. Through surveys and case studies, this project examines how organizations and their partners in the Global South implement feminist approaches into planning and programming.