Multi-SNP prediction model for lung function decline

Chronic obstructive pulmonary disease (COPD) is a 3rd leading cause of death (1) which decreases lung function due to irreversible airway obstruction. The main indicator of the progression of COPD is a rate of the forced expiratory volume of 1 second (FEV1) decline. The intern will build the prediction model for the slope of FEV1 decline and find the genetic variants that affect these FEV1 changes. Some variable selection machine learning algorithms will be applied to screen important genetic variants and the performance of prediction on FEV1 change will be compared.

Breaking Biofilms with Ordinary Polymers – Light-Activated Antimicrobial Crosslinkers

Microbial growth on surfaces, or biofouling, is a pervasive problem across sectors including medical implants, hospital surfaces, water treatment, and environmental monitoring. Many existing solutions involve the use of harsh chemicals that may harm human health or the environment. In this work, our team of chemists, biologists, and engineers will develop a plastic that includes light-activated molecules that prevent microbes from growing, but do so in a way that is site-specific and inherently safe. Our partners, Epic Ventures, Inc.

LiDAR-based object detection and tracking for real-time parking availability monitoring

This project will develop a smart parking solution, including both hardware sensors and the analytics platform, that provides real-time parking availability data, which can support decision makings, such as policy refinements, demand-responsive pricing, etc. Our project aims to optimize the rate of parking facilities’ utilization as well as improving drivers’ parking experience. In terms of urban planning, our solution will reduce traffic congestion, carbon emission, parking-related accidents and frustration, creating a more habitable community.

Research into Convolutional Neural Network (CNN) Explainability

Machine Learning is advancing at an astounding rate. It is powered by complex models such as deep neural networks (DNNs). These models have a wide range of real-world applications, in fields like Computer Vision, Natural Language Processing, Information Retrieval and others. But Machine Learning is not without some serious limitations and drawbacks. The most serious one is the lack of transparency in their inferences, which works against relying completely in these models and leaves users with little understanding of how particular decisions are made.

Assessing and managing acoustic disturbance to bowhead whales in the Canadian Arctic

Scientists of WCS Canada have obtained funding through the Canada Nature Fund for Aquatic Species at Risk (CNFASAR) to conduct a collaborative project focusing on bowhead whale research in the Canadian Arctic. The proposed postdoctoral project is a main part of the CNFASAR project and aims to assess how bowhead whales react to underwater noise so that risks from human activities, particularly ship-related, can be managed effectively.

Automobile Purchasing Behaviorial Data Collection, Management, and Analysis

This project investigates automobile purchasing behavior of female millennials. In order to achieve the goal of understanding and making use of purchasing behavior, data are to be collected, managed, and analyzed. In addition to using existing data and third-party data, two major tasks of data collection are the use of questionnaire and web crawling to gather region, product types, and consumer market segment specific information relevant to the partner organization.

Synthesis of graphene and graphitic films

The overall problem to be addressed is the synthesis of graphene or graphitic coatings from the liquid phase. This work follows from a previous investigation, supported by a MITACS Accelerate grant, into the use of “poly(hydridocarbyne)” (PHC), a soluble carbon- hydrogen polymer, as a precursor for the formation of diamond- like carbon coatings. As a result of attempts to purify PHC by electrochemical cycling it was discovered that the surface of the polymer could be converted into a graphene or graphitic layer.

Photocatalytic Oxidation of Volatile Organic Compounds in Air

A new type of device that uses a combination of UV light, oxygen, flowing water, and titanium dioxide (a com-mon white pigment) is capable of removing volatile toxins from the air. This device will be investigated using a range of techniques that provide molecular insights into this process, and those insights will help in the rede-sign of the device to make it as effective as possible, in terms of energy efficiency, longevity, reusability, range of compounds removed, and speed of their removal.

Winter Ecology of Chinook Salmon in the Canadian Salish Sea

Chinook Salmon are a species of high ecological, economic and cultural value in BC. Recent declines in Chinook Salmon abundance have highlighted a need to understand factors controlling their productivity. One hypothesis suggests that the first winter in the ocean plays a critical role in controlling Chinook Salmon survival, and in turn, abundance. Little research has been conducted during the winter, limiting our understanding of this potentially critical period.

Achieving consistently flavoured sour beers through better chemical understanding

The popularity of sour beers is continuously increasing. Producing sour beers is time consuming and obtaining a consistent flavor profile over multiple batches can be challenging. This in addition to scaling up production to meet customer demands can negatively influence the quality and flavor of the beer. This project aims to develop advanced analytical techniques to help understand the relationship between chemical composition and flavor.