Anomaly detection and simulation for unlabeled sensor data

The rapid development in the areas of statistics and machine learning demonstrate unprecedented performance in making cognitive business decisions. Quartic.ai aims to use state-of-the-art machine learning technology to help manufacturers assess and maintain the quality of their industrial units, which suffer damage due to continuous usage and normal wear and tear. Such damage needs to be detected early to prevent further losses. The data in this domain are recorded using sensors at various stages in the process flow.

Modeling and Measuring Insurance Risks Considering IFRS 17 Framework

The objective of the project is to design a model determining capital requirements associated with property and casualty insurance business lines for an insurer that is compliant with the new IFRS 17 framework (international accounting framework). Several subcomponents of the model will be developed such as a dynamic model embedding dependence for the evolution of incurred but not reported (IBNR) claims, a risk measurement component with risk measures and an allocation framework for capital requirements across the various business branches of the insurer.

Advanced Data Science Research for Social Good II

Municipal governments and urban centres across Canada are being inundated with data—data that have potential to improve public service. Despite this, local governments do not have enough data expertise to extract insight from these overwhelming datasets. Simultaneously, high-quality personnel (HQP) in the domains of data science and urban analytics lack opportunities to work closely with local government to address this gap.

Machine learning applied to drilling in open pit mines

The project involves identifying changes in mineralization during the drilling of the blast holes. During drilling, an experienced driller is able, to a certain extent, to detect signals that indicate that a zone change is occurring: vibration in the cabin, rotation rate, etc.

Quantitative risk measurement techniques for insurers

This project will assist Sun Life Financial to build, implement and validate quantitatively sophisticated state-of-the-art models of its risk portfolio. This will result in a better quantitative and qualitative understanding of company's risk, liability and capital profile, and thus in more effective risk management decision making process.

Cryptocurrency Index Research

Cryptocurrency markets exhibit highly chaotic behaviour, differing substantially from securities. This research project looks at the cryptocurrency markets for data–aiming to answer if it possible to create mathematical models which track the overall behaviour of the Cryptocurrency Market, while minimizing risks. Through this research we expect to reconcile the theory developed above with the real life cryptocurrency exchanges and coins.

Automated Visual Inspection, Sentencing & Dressing

Within the aerospace sector, aftermarket services account for over 50% of revenue generated by aero engine manufacturers. Central to this is the ability to inspect and repair high unit cost components. Many processes are manual but given the ever-increasing quality, cost and delivery requirements, and the safety critical nature of these rotating parts, there is a strong drive towards process automation.

Statistical machine learning methods applied to ATB data for credit risk modelling

Machine learning (ML) is a method of training a computer to learn from data and predict future outcomes based on existing patterns in the data. This project aims to utilize various ML methods as new and potentially better analytics and predictive tools in the area of credit risk management for ATB. Given that data quality and flows change over time, a new framework built on Google Cloud Platform to update the machine learning models will also be developed.

Intraday Trading and Analysis and Monitoring Trader Behavior

Electronic exchanges are venues that provide immediacy for those who need to find a counterparty to their trades. Orders of various types arrive in the market at ever increasing speeds, and in this era of high-frequency trading (HFT), institutional investors are often disadvantaged because of their high-latency relative to faster traders.

Statistical Analysis of Women’s Representation in the Animation and Visual Effects Industry in Vancouver

The creative industry is one of the vital pillars of the Canadian economy. Furthering the careers of women in business, technical and creative roles in Vancouver can help promote the economic growth of BC creative industry and advancing women into higher roles. Our project is to track the real-time data of the female staff in animation and film studios in Vancouver and analyze the data. The methods for data collection include the traditional ways like making survey and doing interview, and the web-based way to make a database linked by a data collection website.

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