Developing Prediction Models on S&P 500 Index using Social Sentiment and News Events

Project is to import ten year’s of historical data on news events, public sentiment metrics and the price movement of S&P 500 related equities for study and analysis through the latest Data Mining and Machine Learning techniques. The goal is to uncover correlation and causality between events and price movement of global markets in multiple timeframes (three hours, daily, weekly, monthly and yearly). Specifically, the research would answer the question which features (metrics) generated from initial news and sentiment data have predictive power and which don't.

An investigation on software quality measurement

Software failure may result in substantial damage, especially to human life and financial loss. High-quality software is recognized as a product that has been specified correctly, and that meets its expected specifications. It is important that the quality characteristics be specified, measured and evaluated. In this internship, the primary objective is to create the software quality deviation artifact through comparing the user expected quality against the final observed quality of a software product. For this purpose, the quality measurement process is focused.

Adaptive User Interfaces for 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 in the process of signing partnership agreements with retailers capable of collecting large-scale datasets of customer behaviour. Through a data-sharing/consulting partnership we plan to perform research on the design of recommender systems customized for the data-sets available to brick and mortar retailers.

Models, algorithms and technologies for the treatment of atrial fibrillation caused by heart blockages

Heart failure is among the causes of death in developed countries. Scientific and medical research has made improvements in treating this condition. Mathematical modelling and computer simulation would help in developing the necessary technologies to detect and treat heart blockages and also give a better understanding and protocols for ablation, the clinical procedure used to treat this condition.

Advancing Visualization for Mobile E-Commerce Clickstream Data

We propose to design and build an advanced visual analytics tool to support the analysis of large-scale e-commerce datasets. This data is generated by software platforms that collect information about the performance of e-commerce systems, consumer behaviour, and messages sent by retailers to consumers. Current e-commerce tools provide only simple overview statistics because of the scale and complexity of this data, but more sophisticated analysis could lead to much more effective strategies for e-commerce engagement.

Manitoba Rural Broadband: A Capitals Assessment in Rural Communities

Researchers have recognized Information and Communication Technologies (ICTs) potential but have not fully understood the benefits of ICTs as ways of strengthening rural communities in places such as Manitoba.

Leveraging data analytics in modern tax function

Investigating geographical footprints of income shifting by multinational enterprises. PwC owns a large data set across all industries in Canada from its tax consulting engagements and annual standard tax filings from clients. This growing data source is an opportunity for accurate tax benchmarking, trend analysis and gaining deeper insights by transforming them into market differentiating knowledge that can be dynamically shared and accessed by multiple teams.

Comparing and Improving Approaches to Topic Modeling

The proposed research project aims at evaluating and improving a technique in Statistical Natural Processing called Topic Modelling in order to apply it to real-life scenarios. Topic modeling is a techniques that allows the quick discovery of what the main topics of a document collection are, and thus automatically answers the question “What do these documents talk about?”.
Several approaches have been proposed to implement topic modeling, but their evaluation have rarely taken the end-use into account.

Graphene Oxide membranes for acoustic drivers

The Graphene Audio group at TandemLaunch is working to revolutionize loudspeaker design through the use of graphene composite materials (Graphene-CMs) in loudspeaker membranes. Graphene is a newly discovered material with exceptional mechanical and electrical characteristics. Its low mass and high strength make it ideal for use in acoustic transducers offering an immediate benefit over existing loudspeaker technologies.
This project seeks to improve the manufacturing techniques and acoustic characteristics of these Graphene-CMs.

Resource Allocation for Cloud-Based Notary Service

Cloud computing has emerged as an important platform for business and companies, providing a cost-effective way to scale business service with users’ demand. The proposed research project aims to take full advantage of cloud computing for legal-service companies in Canada, with a particular focus on cloud resource allocation. Unlike conventional resource-allocation schemes that are either centralized or distributed, the proposed research will develop a new hybrid resource-allocation scheme that enjoys clear advantages over conventional schemes.