Fields-CQAM consists of a network of research training laboratories at Ontario universities addressing challenges in areas of socio-economic importance to the province. Organized and administered by Fields, the labs will be the home of cutting-edge research, expansive graduate training, and a demonstrable ability to develop and exploit new quantitative methodologies.
This unique network of labs link academic disciplines and industry into a province-wide innovation cluster for research and training. The expertise of these labs are committed to ensuring a steadily expanding talent pool trained in the latest quantitative techniques; forging partnerships with industry and the not-for-profit sector; and commercializing quantitative research solutions for receptor challenges for the benefit of the broader economy.
Fields-CQAM is currently undertaking research into the following sectors of strategic importance to Ontario:
Human-machine symbiosis seeks to achieve a deep integration of human and machine, enabling the design of information technologies that conform perfectly to us physically, socially, and psychologically. Our research focuses on developing models of human perception, capabilities, and needs to enable deeper integration, and achieve greater utility in applying information technologies. These models are used to create fundamentally new technologies to enhance human capabilities, such as gesture sensors, user interface software, and technologies to assist hose with accessibility issues living better lives.
Financial data analytics are a critical tool in the finance, banking, and insurance communities. Financial institutions require data analytics in every part of their business, ranking from risk management to automatic loan issuance, to fraud detection and service marketing. Understanding how to covert financial and social data into usable insight requires not only strong financial knowledge but also expertise in data analytics and use of cutting-edge techniques such as topological data analysis. Our work focuses on providing in-depth training in financial data analysis, state-of-art techniques for parameter estimation, and model uncertainties.
Chemical process industries use data and mathematical models for key business purposes including design of manufacturing processes and products, scaling of inventions, product strategy selection, and process automation. Models and data are used to bring new products to market and to improve safety, quality, environmental compliance, and profits. Our research focuses on working with the private sector to develop models for their specific processes and business needs, including optimizing operating conditions, developing control schemes, and developing methodologies to assess uncertainties associated with model predictions and associated business decisions.
Bioinformatics and big data are revolutionizing healthcare. Mathematical tools are necessary to monitor and predict disease progression using electronic health records (EHRs) or wearable devices (e.g. Fitbit), to improve wait times and scheduling of services (e.g MRI) and medical procedures, and to analyze specific diseases such as diabetes, stroke, and heart failure. Our research focuses on developing analytical tools and training highly-skilled personnel in the areas of health informatics, with an emphasis on modelling, computation, bioinformatics, biostatistics, data analysis, and experimental design.
Developing modelling frameworks and standardized procedures to inform public health and vaccine production decision are critical to strengthening Ontario's capacity for rapid response to emerging public health issues and vaccine industrial production needs. Our research focuses on creating an interdisciplinary interface in four key target areas: acute respiratory infections, vector-borne diseases, food-borne pathogens, and antimicrobial drug resistance. In addition we train students and health professionals in statistical analysis, optimization, coding, and algorithms that are relevant to predicting and prevent disease spread and providing advice to public health agencies and industry collaborators.
Prediction of anomalous or rare events, such as the automated identification of fraudulent financial transactions, the prediction of novel microRNA molecules within the human genome, or the correct diagnosis of rare cancerous biopsies, is both important and challenging. Our research focuses on the development and evaluation of statistical models and machine learning systems for mixed mode data, and on making predictions in the presence of class imbalance, with applications for biomedical informatics, network security applications, sports analytics, and business informatics. In addition we offer training to address the unique challenges associated with applying statistical machine learning for the prediction of rare events.