Undergrad Student Needed To Model Food Fraud

Are you a University of Guelph undergraduate student looking to complete a senior independent study course this coming fall? Do you have an interest in statistical modelling and computational sciences? Do you want to work on an interdisciplinary team? If so, this project might be for you.

We are currently seeking a MATH*4600/STAT*4600/CIS*4900/CIS*4910 (or comparable) student for the fall semester (2020) possessing a strong background in regression analysis (STAT*3240), risk assessment (STAT*3510), computational statistics (STAT*4000), and R programming to assist with the development, optimization, and deployment of a novel regression model to predict time-of-occurrence of seafood fraud in the Canadian supply chain.

This project is a collaboration between researchers in the School of Computer Science, the Department of Integrative Biology, and the Department of Mathematics and Statistics. 

This project may also involve a paid research position for the successful undergraduate student pending a successful result from a recent funding application.

If you are interested in this position, you can contact me using the contact form below.

Project Description

Seafood fraud is a mounting issue facing society today, where both economic loss and substantial harm to fish populations are immediately felt. One aspect of food fraud pertaining specifically to the Canadian market is seafood product mislabelling. Estimates suggest mislabelling rates of 20% and higher throughout the supply chain. Further, both the when and where of consumer product mislabelling remains largely unknown. Species mislabelling manifests at all times of year and involves many key stakeholders. Strong correlations also exist with species stock abundance, market price and conservation status complicating reliable fraud prediction. The current project seeks to address the “when” aspect of seafood species mislabelling by developing a novel Bayesian model in an effort to forecast time-of-year of fraudulent behaviour. Our model will greatly aid regulatory agencies in targeted and timely product testing in an effort to improve seafood authenticity and traceability within Canada.

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