Mobile health (mHealth) apps allow patients to practice self-care and manage their chronic diseases. Common functions in mHealth tools allow users to monitor their symptoms and mood, keep a thought diary, track medication use and trend information; this provides data that can be used to better understand patient behaviour to ensure that patient needs are being met. By using a user-centered design approach for app design, the patient experience is captured through understanding their goals and challenges as well as their journey in living with or recovering from chronic disease(s).

The present study investigates the impact of Eurasian honeybees on the functional diversity and reproductive ability of native stem-nesting bees. Honeybees have the potential to compete with native stem-nesting bees, however, currently no studies have examined this interaction in North American temperate forests. The main goal of this project is to develop a more mechanistic understanding of bee community composition and distribution, in particular, under the threat of exotic introduction.

In this project, we will apply machine learning to perform image style classification. We will build a system that uses image style classification to increase user engagement in an eCommerce platform setting. We will study the effects of user preferences for particular image styles on their engagement with the platform.
Image style classification is the task of categorizing an image based on attributes such as composition style (e.g., minimal, geometric, etc.), atmosphere (hazy, sunny), or colour (pastel, bright).

The primary goal of this project is to explore a variety of new and existing Natural Language Processing (NLP) techniques to improve the performance, and further the automation of, Knote’s text analysis software – specifically with entity recognition. Entity recognition is the process of identifying all groupings of words in a collection of documents that fall within that entity’s purview, such as proper names or chemical compounds.

This project proposes to explore and implement a method of storing and retrieving data relating to genetic variation across a population of individuals. Due to the large amount of genetic information each person possesses, such a database requires special attention to minimize the amount of data stored and to create efficient methods of accessing the data. This work will research and test different strategies to build a compact data store that will return results quickly. This data store will be incorporated into the PhenoTips software provided by Gene42 Inc.

Kobo is an online e-book retailer that provides recommendations for future purchases to its user base. One difficulty that recommendation systems face is what is known as the “cold-user” problem. In this scenario, when we know so little of a user’s preferences (for example, if they are new to the platform), we do not have any basis for recommendations. The goal of this project is to develop an interactive application that can elicit such preferences from users about whom we have little information, and that can help improve recommendations for power users.

The aim of the project is to predict future customer demand for repeat-buying items based on available customer purchase records. However, the purchase history for a single customer may not be sufficient to base predictions on. Also, some purchase records might be missing due to sales events at competitors’ locations. Thus, treating each customer as a replicant of the average customer and averaging inter-purchase times to predict future demand will likely be an inadequate approach.

The proposed project is a characterization study on chitin nanowhisker nanocomposites. Chitin nanowhiskers are derived from chitin, a naturally occurring biopolymer found in arthropod exoskeletons, and offer great potential for reinforcement and property enhancement once blended with typical engineering plastic matrices. Compared to traditional inorganic fillers such as carbon nanotubes and graphene, chitin nanowhiskers are biocompatible and biodegradable, exhibiting comparable property improvements with none of the downsides of the inorganic materials (i.e. biohazardous, toxic).

Epilepsy affects an estimated 50 million people worldwide. These people can experience unexpected seizures that makes it risky for them to engage in everyday activities like driving and walking. A portable wireless neuromonitoring headset prototype that is worn on the head has been developed by Avertus Inc. to address this issue. The headset is designed to read brain waves, and, through a wireless connection to a cell phone, warn the wearer that the device has measured brain activity characteristic with an oncoming seizure.

Perimeter Medical Imaging (PMI) has developed an investigational imaging device to aid in achieving clear margins during surgical oncology procedures. This project will employ PMI’s device to image multiple types of human tissues, which have been previously removed during elective or medical procedures. This study will correlate the images obtained using PMI’s device with the true microscopic structure of the tissue, as confirmed by a pathologist.