Cancer is a disease caused by healthy cells which have acquired genomic mutations enabling these cells to uncontrollably proliferate. As the disease progresses, the population of cancer cells rapidly expands causing tumours. During this expansion the cancer cells also acquire new mutations which lead to genetically distinct sub-populations within tumours. These sub-populations of cells can have different proliferative potential, ability to metastasize and susceptibilities to treatments. Clinically this is a very important issue as treatment strategies need to be tailored to target and eliminate all of these sub-populations, otherwise relapse will occur when new tumours are formed by the remaining cancer cell sub-populations. My research is primarily focused on using high throughput genome sequencing technologies to quantify the diversity of these sub-populations and study their dynamics across time and space. The current generation of genome sequencing technologies allow us to simultaneously sequence the genomes of thousands or millions of cells in a single sample. Unfortunately it is very difficult to use this data to directly infer the sub-population structure as we cannot identify which fragments of DNA came from which cells. I will be developing statistical and computational models to solve this problem. I will then be applying these tools to study tissue samples taken from different regions of tumours or from metastasis and recurrence events, with the goal of understanding how cancers evolve. This work will provide novel insights into the basic biology of cancer which ultimately will be useful in the development of personalized treatment strategies.