Implementing Factor Models in Investment Management

The internship will consist of studying, building, implementing and testing so called factors that are used to characterize the equities, commodities and currencies that the company invests in. These factors can be thought of as characteristics relating a group of securities that is important in explaining their returns and risk. My task will be first to understand the risk factors that are of particular importance to the company’s investment strategy.

Identifying and Quantifying Analytes in Real Life Environments with Chemical Noise

Developing smart technology determines the future economy of societies nowadays. Electronic nose is a device that audits the chemicals and transforms it to human odor perception. One of the most challenging steps to transform electronic nose to smart nose is its patter recognition machinery, because electronic nose data are imprecise and noisy. This pattern recognition machinery builds an empirical statistical model using machine learning algorithms over electronic nose data, to transform the these data to human odour perception.

Real-Time Radar Data Analysis for Classification of Ground and Aerial Targets

Radars are being used more and more in critical sites such as airports, military bases and borders for surveillance of huge areas to detect unwanted intrusions. Determination of the type of each target is essential for such systems to identify the nature of the intrusion and avoid false and nuisance alarms. This thesis is focused on the design of automatic target classification systems based on analysis of real radar data from different sites and environments.

Assessing Participatory Management with a 5S project – An Empirical Approach

This Mitacs Project’s aim is to calibrate and test a model to assess and improve the employee’s engagement. The work will focus in define a methodology to identify the necessary data for the calibration and the installation of a model (a set of equations) for improvement of a factory. This project will benefit both: the researchers with data for calibration and the factory with a new tool to improve productivity.

Machine learning in fluid composition quantification

A critical issue in the oil and gas industry is to quantify the composition of fluids flowing back from the hydraulic fracturing process. This quantification is usually carried out by a manual process (frequently via a visual test) to estimate the water and oil produced from a well flow back process. A sample of these onsite tests are sent to laboratories for chemical analysis. This process has been the status quo for decades. This approach is manual, prone to error, and does not lend itself to sophisticated real time analysis.

Modelling challenges in the fundamental review of the trading book

Upcoming changes to financial regulation and oversight are creating increased demands for the accurate measurement of financial market risks and the provisioning of adequate economic capital to ensure that financial institutions can withstand market shocks and extreme events. The objective of this research project is to study issues related to the theory, performance, and practical implementation of standards and requirements for measuring and managing market risk set out by the Basel Committee on Banking Supervision.

Proxy models for Thermal Production Optimization

Amid the tough challenge of dwindling oil prices, GE is seeking for new technology to create production forecasting and optimization tools that simulate the real operating environments and optimize across the entire process, providing actionable insights that help producers achieve their cost, production, and environmental goals. The objective of this project is to develop data driven models for optimizing bitumen production in SAGD reservoirs.

Development of a hybrid seismic data inversion method for determining well-drilling location at complex geophysical area

Due to the current economic downturn, especially the lower crude oil price, the drilling success rate become the most important goal for any oil/gas company. For a start-up company, any failure in drilling will be a disaster. To this end, the Deep Treasure Corp wishes that through the combination of mature hydrocarbon prediction techniques and new research results in seismic inversion,  the success rate of hydrocarbon prediction, the theoretical basis for well placement can be provided  in Roncott field, which will improve the success rate in drilling.

Production planning at Wesgar

Wesgar is a factory that produces metal sheets for its customers. After a product is ready, it will be delivered to the customer. The objective of this project is to improve the On Time Delivery. At Wesgar they have different machines in their production system. These machines are able to process different products based on the shape, size and material. Each product must pass some specific machines to be processed through the production plan. A schedule that determines which product must pass which machine at what time is required for the production system at Wesgar.

Complex Continuing Care in a Rural Hospital: Optimizing community-based health care

Community hospitals in small towns or rural areas face challenges in delivering health care that will allow elderly members of their community to remain in the community that they helped to build. Using simulation modelling, this project will develop strategies for delivering complex continuing care in rural hospitals that is closely integrated with long-term care, residential care, and home care services. Small towns and rural communities have a tight-knit social fabric and the contributions that family support and community services provide to health care are important factors.