Postdoctoral fellow: Dr. Raluca Eftimie, Pathology and Molecular Medicine, McMaster University
Lead faculty member: Dr. Jonathan Bramson, Pathology and Molecular Medicine, McMaster University
Cancer emergence and progression are highly complex processes characterized by interactions among a large variety of cells and signalling molecules. It is very difficult to explain these complex interactions through linear thinking and molecular reductionist approaches. Mathematical modeling is a powerful tool that can substantially enhance our capacity for interpreting the data and generate new hypotheses.
Postdoctoral fellow: Dr. Paul Nguyen, Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto
Lead faculty member: Dr. Patrick Brown, Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto
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.
Real Time 3D Reconstruction of Breast Microwave Multistatic Radar Images using Adaptive Holographic Technique
Postdoctoral Fellow: Dr. Daniel Flores-Tapia, Department of the Mathematics, University of Manitoba
Lead faculty member: Dr. Kirill Kopotun, Department of the Mathematics, University of Manitoba
Breast Microwave Radar is a promising new technology for breast cancer detection. Nevertheless, current image formation methods face issues that limit the use of this technology in clinical scenarios. The goal of this project is to use mathematical modeling and analysis to develop a novel image formation method for breast microwave radar suitable for use in realistic breast imaging settings. This technique will be capable of generating accurate and high contrast images for a specific patient in real time.
Postdoctoral Fellow: Dr. Yildiz Yilmaz, Dalla Lana School of Public Health, University of Toronto
Lead faculty member: Dr. Shelley Bull, Dalla Lana School of Public Health, University of Toronto
The objective of the project is to develop, evaluate and apply informative statistical methods to the task of identifying novel genes/pathways involved in breast cancer recurrence. A model for time to cancer recurrence using clinical, pathological, and molecular measures in the setting of high-dimensional genome-wide genetic scans will be developed that allows for a proportion of the patients to be long-term survivors.
Dr. Jack A. Tuszynski , University of Alberta
Project CyberCell Inc.
Technology Innovations, LLC
National Institute for Nanotechnology
Cross Cancer Institute
McBride Career Group
Howard J. Greenwald P.C.
Multimedia Advanced Computational Infrastructure (MACI)
Canadian-European Research Initiative on Nanostructure (CERION)
Dr. Shelley Bull, University of Toronto
Complex traits, such as susceptibility to diabetes, cancer or tuberculosis, which vary in human and natural populations, are determined by multiple genetic and environmental factors that interact with one another in complicated ways. This interaction depends upon population characteristics as well as characteristics of the individual and the family.