Health

This project aims to develop methods and software for performing spatio-temporal analysis of cancer incidence and smoking data in Ontario over long time periods with high spatial resolution. This will allow changes of cancer incidence over time to be better understood, and accommodate rare cancers that require long study periods in order to accumulate data. Because of small counts common to small area analysis, computationally intensive Bayesian inference methods will be needed.

This research develops a comprehensive methodology for the integrated optimization and control of human-friendly robotic technology that will be applied for the advanced healthcare and biomedical manipulation. Some original ideas, methods and algorithms are proposed in this research based on several novel mathematical models, which will benefit the development of general robotics in the direction of safety with high performance to human beings.

Using modern statistical measurement error methodologies and analysis, we aim to efficiently and accurately discover and characterize predictive models of responses associated with abnormal growth development in young children and adults. This proposal is unique in scope and vision by addressing health issues that threaten the sustainability of the health care system.

Magnetic Resonance Imaging (MRI) is an important medical imaging technology for clinical diagnostics. However, its slowness in data acquisition poses major problems in practice. In recent years, many research efforts to accelerate MRI data acquisition were based on the compressed sensing (CS) theory. CS is effective for signals that have highly sparse representations. However, it suffers from high computational complexity and lack of performance stability.

Due to the explosive growth in the technology for manufacturing integrated circuits, modern chips contain millions of transistors. Using sophisticated optimization algorithms, it is possible to achieve notable increases in the performance of the chips, reduce the manufacturing costs, and produce faster, cheaper computing for society. Thus, the objective of this project is to enhance the solution of large-scale optimization problems arising in these applications.

Bacterial biofilms are microbial depositions on immersed surfaces and are ubiquitous in natural and engineered environments. For example, they play a significant role in medical applications where they can grow on artificial implants and cause infections; they form dental plaques and contribute to tooth decay; they can be utilized to assist in clean-up of contaminated soils or groundwater aquifers; they accelerate corrosion of metal surfaces; and they are a main culprit behind contamination of drinking water systems and food processing equipment.

Diseases such as diabetes, Alzheimer's, HIV and blood disorders present challenges to our society, our healthcare and our basic scientific understanding of physiological processes within the human body. Mathematical modelling can be used to help scientists decipher the processes at work in these complex diseases at a molecular, cellular and organ level. Recently, research team members examined the ways in which drugs such as Filgrastim could be used to replenish levels of white blood cells, a common challenge following chemotherapy.

When patients do not use medications as prescribed, the drugs may lose the ability to treat the disease. However, the impact of variations in patient use is not generally studied during clinical trials. By identifying the reasons for patient non-compliance and individual patient modifications, the team will determine the impact that poor compliance or dosing regimen adjustments have on therapeutic failure. A quantitative analysis of this impact will be developed. Initially, the team will focus on oral chemotherapy where concerns about compliance have become an important issue.