Complex Continuing Care in a Rural Hospital: Optimizing community-based health care

Community hospitals in small towns or rural areas face challenges in delivering health care that will allow elderly members of their community to remain in the community that they helped to build. Using simulation modelling, this project will develop strategies for delivering complex continuing care in rural hospitals that is closely integrated with long-term care, residential care, and home care services. Small towns and rural communities have a tight-knit social fabric and the contributions that family support and community services provide to health care are important factors.

Statistical and Machine Learning with Applications to a Hybrid Recommender System for Retail Data

We are in the process of creating and growing a team of researchers expert in the field of machine learning and data-mining. Ultimately, our aim is to create solutions to eliminate the need to manually define personalization strategies. We are in the process of signing partnership agreements with retailers capable of collecting large-scale datasets of customer behaviour. Through a data-sharing/consulting partnership we plan to perform research on the design of recommender systems customized for the data-sets available to brick and mortar retailers.

Models, algorithms and technologies for the treatment of atrial fibrillation caused by heart blockages

Heart failure is among the causes of death in developed countries. Scientific and medical research has made improvements in treating this condition. Mathematical modelling and computer simulation would help in developing the necessary technologies to detect and treat heart blockages and also give a better understanding and protocols for ablation, the clinical procedure used to treat this condition.

Constructing data-adaptive dictionaries for robust sparse feature selection in classification of noisy electro-dermal activity data

Hypersensitivity to sensory stimuli causes overstimulation, inducing overwhelming emotional distress in individuals with an autism spectrum disorder (ASD). Reveal is a wearable device designed by Awake Labs that monitors anxiety levels in ASD children and interfaces with parents and caregivers. It predicts behavioural “meltdowns” by tracking and classifying key physiological markers of anxiety using machine learning technology. However, the features between which this model is trained to differentiate were developed ad hoc, and built from data that was collected from adults without ASD.

A life cycle impact assessment methodology based on planetary boundaries

Planetary boundaries can be understood as limits for the Earth’s tolerance towards environmental impacts in the form of, for example, greenhouse gas emissions, water use and the release of nitrogen and phosphorous. This project aims at making planetary boundaries useful to the environmental management within companies. This will happen by developing a method that quantifies environmental impacts of a company in the language of planetary boundaries.

Operational analysis and optimization of the delivery of HIV treatment and care in Vancouver

The continuum of HIV care is highly complex. It includes prevention, testing, patient care, treatment, and support services. This project will help Providence Health Care utilise its limited resources to provide the best treatment and care for people living with HIV in Vancouver. Care for HIV patients includes antiretroviral therapy, treatment of co-morbidities, monitoring clinical markers of disease progression (CD4 count and viral load), and support services to ensure treatment adherence and retention in care.

Prediction of user intentions based on video captured interaction

User intention prediction of event data, collected during the business process, has become an important topic in Web Analysis and Business Intelligence. Commercial organizations have realized its importance for providing cost-effective opportunities to improve their decision-making in digital marketing strategies. We aim to develop and implement a statistical prediction model to make the prediction of user intention, using the retroactive video tracking data, while the anonymous customer navigates on the website.

Application and investigation of new material in tandem solar cells with enhancing IR spectral absorption

Solar cells which convert solar energy directly into electricity are among one of the most viable solutions to the world’s foreseeable energy crisis and global environmental issues. One key strategy to improve the efficiency of solar cells is to enhance the overlap between their absorption spectra and the solar spectrum. When two or more subcells with distinct and complementary absorption spectra are stacked, the tandem solar cells are created and a broader range of the solar spectrum can be absorbed and more solar energy can be harvested.

Applications of Neural Network Curve Fitting Methods for Least-squares Monte Carlo Simulations in Financial Risk Management

Monte Carlo simulation methods are commonly used as a risk management tool to estimate the risk exposure of financial asset portfolios. However, the traditional brute-force Monte Carlo (BFMC) method is often very timeconsuming, which makes it difficult to serve the risk management needs of modern insurance industry. An alternative approach, the least-squares Monte Carlo (LSMC) method, could substantially reduce the computation cost by fitting a proxy function of liabilities using simple nonlinear regression methods.

Scenario optimization for robo-advisory analytics.

In recent years, improvements in technology provide the opportunity for investors to use computer algorithms to produce low-cost guidance on possible portfolio investment mixes and strategies. This project is directed at the research and development of one such “Robo- Advisor” algorithm based on forward-looking scenario optimization, in order to determine the efficacy of the strategy. Here optimal portfolios are selected based on investor’s views on future scenarios, goals and risk tolerance.