Automated Detection and Classification of Adverse Events in Surgery

During surgeries, it is important to keep track of what is happening with the patient, the steps being taken during the surgery by the operating staff, and unforeseen events that occur. All the previous correspond to the surgical workflow. Keeping track of the workflow is essential to achieving a better and safer surgery. In the past, computational tools have been developed to track each step the surgeon takes during the surgery, and dividing the separate surgical phases. However, the adverse events have not been tracked.

Extracting supplier information from the web

Using web crawling technology in coordination with state of the art machine learning techniques, the project aims to mine useful, structured information about the world’s suppliers from the web. Recent advances in artificial intelligence have increased the viability of such autonomous systems for extracting coherent information from arbitrary human-produced content. By leveraging these technologies, our goal is to build improved supplier discovery and recommendation systems.

Generative Models for Financial Time-Series Predictions

The intern will work on applying new advances from the field of Machine Learning to models which make predictions about time-series data. The models have the desirable property modeling the distribution of outcomes in a way that we can sample from, allowing us to account for uncertainty in the model’s predictions. By making more accurate predictions with more accurate gauges of uncertainty, Electronica will be able to construct portfolios which give more desirable risk-adjusted returns to investors.

Automated CNC processing of complex and high-aspect-ratio microfluidic devices for biomedical applications

Disposable microfluidic devices, also known as labs-on-a-chip, made out of plastic materials have seen increasing applications in chemical and biomedical analysis. In most applications, microfluidic devices usually incorporate small channels and chambers for micro sized dimensions, using heights between a few hundred to a few micrometers. Currently, manufacturing processes have been established to create these sub-millimeter deep features. However, in other applications, higher (or deeper) features of a few millimeters may be needed.

Increasing Patient Engagement and Informing Marketing Decisions through the use of Patient Personas and Patient Journey Mapping

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).

Effects of Introduced Honeybees (Hymenoptera: Apidae) on Native Stem Nesting Bees (Hymenoptera: Megachilidae) in Temperate, Mixed-wood Forests

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.

NLP Techniques for Automated Entity Recognition

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.

Image Style Classification and Its Application on User Engagement

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).

Construction of a Genetic Variant Store

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.

Interactive preference elicitation application for book recommendations

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.