The goal of this project is to help build artificial intelligence algorithms for the diagnosis of disease using data derived from the human microbiome. This project will be focused on implementing new statistical methods to reduce “noise” found in data from different sources, allowing for us to improve the training of artificial intelligence algorithms. Another focus of this project will be to implement new types of models that are better suited for microbiome data, allowing for more accurate predictions.
This research project sets out to conduct a comprehensive literature review on Indigenous agriculture in Canada. We collect information about different sources of data that could be put in use to provide insight into Indigenous engagement in the Agriculture/Agri-food sector across the country. We will review the current Statistics Canada’s databases to explore data overlaying options to compile new datasets on Indigenous engagement in the Agriculture/Agri-food sector. Using these data, we will map Indigenous agribusinesses across Canada.
Decision makers responsible for managing public and population health risks are increasingly faced with challenges in integrating information from multiple sources to support evidence-based risk decision making. To meet these challenges, a new framework for evidence integration will be developed to provide guidance on how best to synthesize information from all relevant sources to support the best possible risk decisions. This framework will also provide guidance on the use of big data in evidence integration.
The proposed project implements the applications of virtual view of indoor environments and objects in 360 degrees, thereby encouraging people to check the information of rooms and products online during the COVID-19 pandemic. Specifically, six objectives are proposed, each undertaken by two interns, to build a virtual view system satisfying different scenarios.
Cancers are heterogeneous disease that hijack many of the body’s normal biological processes. Additionally, tens of thousands of genes are involved in each person’s normal biology, while only a fraction of those are repurposed by cancers to drive disease. At an individual level, utilizing entire transcriptomes is rare, as there is too much information for clinicians to process. However, not using this resource can mean important genes and processes are missed. Identifying the set of genes that drive a patient’s cancer would improve therapy design, patient quality of life and outcomes.
Risk aggregation is omnipresent in insurance applications. A recent example, borrowed from the modern regulatory accords, is the determination of the aggregate economic capital and its consequent allocation to risk drivers. A more traditional illustration of the importance of risk aggregation in insurance is the celebrated collective risk theory that dates back to the early years of the 20th century. This project will assist Sun Life Financial to build and implement an efficient quantitative framework to approximate the aggregate risk of its portfolio.
The Fundamental Review of the Trading Book (FRTB) is a set of regulations by the Basel committee, which is expected to be implemented by banks by 2022. The regulation targets market risk management in banking industry. According to FRTB, banks need to post extra capital against non-modellable risk factors, which could account for 30% of total market risk capital requirement. Reducing the weight of non-modellable risk factors can greatly reduce the required capital and thus increase banks’ profitability.
This project is about implementing a technique called Agent-Based modelling (ABM) so it can work better in real-world application. Particularly, it aims to help policy makers to do more adaptive decisions when the whole economics environment changes. For example, how to set the federal interest rate after COVID-19 panic? This model could simulate how all kinds of people, regulators, corporations, banks, or investors interact with others and how that interaction could cause specific things to happen to them and to the market more broadly.
Following the success of mathematical and statistical modelling in various financial markets, we believe that quantitative methods can also be used to effectively establish trading vehicles for power and its derivatives. However, most of the quantitative literature in power markets is focused on specific aspects primarily from the perspective of load-serving or generation units. Instead, we aim to build a quantitative power trading framework which expands the activities of Plant-E Corp in North-American power markets and fills in the current gaps within the literature.