Embedding Project

The Embedding Project is a public-benefit research project that relies on strong social science research methods to bring together thoughtful sustainability intrapreneurs from across industries and around the world, and harnesses their collective knowledge to develop rigorous and practical guidance that benefits everyone. This internship will offer an MBA student the opportunity to gain experience in both practice and research, while learning from leaders in the field.

Data Analysis and Consolidation for Aircraft Parts Manufacturing

Avcorp Industries provides the world’s leading aircraft manufacturers with supply chain solutions and repair support. Yield optimization, predictive maintenance, and equipment calibration are needs that are widespread throughout the manufacturing industry. The root cause of failures in product testing is often difficult to determine particularly when the failure signals are sparse relative to the available background data. Compounding the problem, the process must meet a variety of specifications for multiple customers simultaneously.

Machine-Learning-Based Artistic Photo Manipulation and Stylization on Mobile Devices

Recent advances in using machine learning for object recognition and image manipulation have resulted in a new and emerging market for mobile applications that use machine learning for creating a variety of new artistic expressions. This research will develop a framework for performing machine-learning-based photo and video manipulation on mobile devices with the goal of integrating it with the Generate Toolkit. This proposal follows previous MITACS internships between the same partners and further extends our objectives.

Impairment screening utilizing biophysiological measurements and machine learning algorithms

In this project, a comprehensive testing station for impairment screening will be implemented. The station includes an eye testing goggle, movement detectors, biophysiological measurement sensors, and an integration algorithm to integrate the result of measurement to diagnose the status and type of impairment. The hardware technology resides at the industry partner while this project is focused on implementation of data gathering and data storage platforms, feature extraction and selection algorithms and machine learning algorithms to quantify levels of impairment.

Preventing Risk for Metabolic Syndrome in Workaholics: An Intervention

Tendencies towards workaholism have been linked to poor health and increased risk for diabetes and other chronic condition. A health improvement program that is interwoven within the workplace and leverages the ubiquitous use of smartphones has good potential of benefiting the workforce. The aim of this research project is to evaluate Transform, a digital health program created by Blue Mesa Health. The program is designed to prevent diabetes by helping people adopt healthier lifestyles.

Improving avalanche forecasts in data-sparse areas with physical snowpack modelling

Assessing dangerous avalanche conditions requires a reliable stream of weather and snowpack data, which can be difficult and expensive to collect in many remote areas of Canada. Snowpack conditions can be simulated in these areas by coupling weather forecast models with physical snowpack models, however, this method has had limited adoption by avalanche forecasters.

Design of the next-generation of content-based, context-aware product recommender systems

We are in the process of creating and growing a team of researchers expert in the field of machine learning and data-mining. Ultimately, our aim is to create solutions to eliminate the need to manually define personalization strategies. We are working with more than 1000 retail locations across North America and collecting large-scale datasets of customer behaviour. Through a data-sharing/consulting partnership we plan to perform research on the design of recommender systems and predictive models customized for the datasets available to retailers.

Hydraulic Vortex Optimization of a Recessed Impeller Pump

Toyo Pumps is performing research to upgrade their current line of recessed impeller or vortex pumps. Currently, these pumps can only operate in very limited and specific conditions. The goal is to redesign these pumps to allow them to operate in a wider variety of conditions and improve the efficiency, producing more work at a lower power cost. New designs will be made using advanced technology that makes it possible to virtually investigate hydraulic designs using software that simulates the flow through a pump.

Examining the barriers and facilitators to trauma registry sustainability in the Global South

Injury is a leading cause of death and disability world-wide, however rates are especially high for low- and middle-income countries (LMICs). Trauma registries— databases that document information on the injured patient related to the injury event, demographics, process of care, and outcome— are commonly used in high-income countries and have proven extremely effective in reducing rates of death and disability through informing injury prevention and quality improvement programs.

Understanding Real-time Particle Systems for Health, Entertainment and VR

The proposed research is a collaboration between Persistant Studios’ PopcornFX and SFU’s iVizLab to collaboratively work on ways to understand the processes involved in content creation using a real-time particle system. The iVizLab’s research focuses on using real-time visuals with the biodata from the users as one of the main interfaces to create affective systems that can intelligently interact with the users. In creating the visuals for the iVizLab, it is important to be able to create content that can be modified in real-time with the incoming data.