Inference, Understanding and Action Through
Machine Learning, Statistics and Beautiful Visualizations
I'm a data scientist interested in exploring ideas and solving challenging problems with data. In January of 2019 I received my PhD from the Department of Biological Sciences at Simon Fraser University studying river networks and climate change. My research revealed river network structure controls patterns of climate change impacts on river ecosystems.
Working at spatial scales the size of the United Kingdom and over time periods spanning decades, I've become adept at gathering, munging and integrating high volume data of various types and building models of inferences.
I've also become keenly interested and adept at data visualization. Communicating the scope of a problem, the results of an analysis or the limitations of an algorithm is essential for maximizing the value of data. In my work I aim to build beautiful, succinct and interactive figures that encourage engagement and facilitate understanding.
Munging through buried, dirty data is a challenge. In my work studying river temperatures, the data was often literally buried, or blown on a river bank, or simply bobbing between measuring air and water temperatures with the seasons rains.
In the world of big data, finding automated ways of cleaning data will be the difference between success and failure, finding useful and actionable insights and making costly mistakes.
In my work I aim to succinctly describe data in a way that is appealing to the eye, thereby engaging the audience. Engagement is the key to understanding and if the image is beautiful you can include more information without overwhelming and exhausting the viewer.
The figures found in this section are a few examples of my attempts to make simple and intuitive big ideas that leverage huge amounts of information.