Signalling Bodies as Resistant: Coded Queerness in Visual Culture

Through historical research, I examine the role of queer history to demonstrate how, and in what ways, oppression evolves into resistance. Focusing on visual culture, such as photographs, home video’s and films, as well as ephemera, letters and personal papers, the goal is to shed light on the dark corners of queer history. By illuminating historical pain and trauma, the intention is to reveal the resilience of previous generations, and where negative affects inform locations of recuperation.

Security Risk and Control Modeling for Deep Learning using the SAGETEA Methodology

SageTea Software will contribute expertise in working with Smalltalk and the SAGETEA model. This includes demonstrating the current database model and how it works. SageTea Software will also demonstrate its current implementation of Deep Learning libraries on the Python side including Tensorflow, Kibana and Elastic Search. We will provide expertise in the SAGETEA methodology and assist the researcher with developing additional mathematical analysis, software analysis design and coding. We will assist with testing and also supply infrastructure including cloud environments and software tools.

Assessing COVID-19 Impacts on Urban Travel and Activity Patterns Employing Cellphone Travel Data

COVID-19 impacts on travel are unprecedented, affecting virus-spread, transportation services delivery, and how people will eventually safely participate in economic, educational and social activities. These impacts vary substantially across neighbourhoods, often worsening existing inequities in Canadian cities. This project will accelerate research for deriving insights about COVID-19 from TELUS network location data. Specifically, it will develop new methods to use cellphone traces to measure, model, and evaluate our response to COVID-19’s disruption of daily activity/travel participation.

Applying machine learning techniques for demand forecasting in retail

An important component to every growing retail business is demand forecasting which can affect the strategic plans of a business. The impact extends across the business’ function including budgeting, financial planning, price optimization, sales and marketing plans, capacity planning, staff management, risk assessment and mitigation plans.
In this project, we want to apply machine learning technologies to improve the accuracy and granularity of retail demand forecast.

Quini Machine Learning Wine Recommendation Engine

Quini is developing a revolutionary system that allows wine producers to predict with a high level of accuracy how much acceptance and sales they will be able to generate from a wine product, over time, in which major cities and selling to whom as the primary buyers. The system will also give consumers exacting wine recommendations that suit their personal taste and that are likey to be available for purchase in their area.

Reinforcement Learning for anomaly detection in real-time camera feed

How to automatically monitor wide critical open areas is a challenge to be addressed. In this project we are looking for using CNN+LSTM technique for identifying anomalies and by using a deep reinforcement learning approach, classify them into one or more groups such as health, crime, accidents etc. This project aims to alleviate this problem by using deep learning reinforcement algorithms to emergency conditions in a video feed. In this way, the intern should work on this real-time data to, at first, finding anomalies from the live video, then, categorize them into relevant classes.

Sensors data transmission with the Internet of Things (IoT) for water purification systems in indigenous communities

The major contributing factor to waterborne outbreaks in Canada in small drinking water systems is the operators’ lack of technical expertise. Training of small system operators do not cover hands-on training specific to the treatment technologies used in their plants. A simplified smartphone app with real-time monitoring can assist the operators with the decision making process. Aqua Intelligent Technology Inc. is providing this smart solution for small water treatment systems. The vital step in this technology is receiving real-time data from the sensors in such facilities.

Studying Gameful Design for Bite-Sized Information Consumption

MLD Solutions are facing the challenge of creating engagement with their online platform Mozaik.Global that allows users to create, distribute, and sell interactive digital content. This content is created in bite-sized units, currently visualized as cards. The key problem with this new type of digital content is that the company currently does not know how to make this content engaging. To address this problem, we will study the impact of gameful elements to motivate use of Mozaik.Global.

Lives with Houseplants in Times of the COVID-19 Pandemic

This is a research project that will explore human’s relationships with houseplants in times of the COVID-19 pandemic and unpack the psychological, sociological, and environmental significance of these relationships. By digging deeply into our relationship with houseplants and creatively telling stories about them, the project seeks to uncover and deepen our connection with nature in times that we are deprived of it, build connections with living things in times that we can’t connect with other humans, and nurture intimacy in times that intimacy is scarce.

Deep Learning based Real-time Object Recognition and Tracking for Immersive Training and Maintenance Applications

The immersive software market, which includes virtual, augmented and mixed reality, is expected to see tremendous global growth over the next five years as players from all sectors race to identify and capture market opportunities of the technology.
This project investigates into the business and technical aspects of immersive training and maintenance applications by taking various case studies.