Improving the Power of Statistics
You may not understand statistics as well as Dr. Daniel Simpson, but you can be sure his research will help you use them effectively.
As Canada Research Chair in Spatiotemporal Modelling, Simpson applies his statistical expertise to building models—math that takes large amounts of data and calculates the chance of other events happening. By developing best practices and user-friendly statistical tools to solve practical problems, he’s making this type of math more accessible to and usable by everyone.
For example, most fields of science—including climate studies, public health and space science—rely on statistics to make predictions. Everything from predicting flu outbreaks to monitoring air quality to managing fish stocks relies on advanced statistical modelling.
In such cases, researchers are analyzing large amounts of information where the location and time of the observation is important, including events that happen very briefly and only within one region. Deciding how to pick the right statistical model, build it correctly and ensure new data are integrated correctly over the lifetime of a study is challenging.
Simpson and his research team are addressing these issues by creating advanced modelling pipelines that allow the input and analysis of increasingly complex, real-world data. From incorporating experimental design and data collection to visualization and computation, his statistical models are feasible, flexible and freely available in open-source libraries.
Ultimately, Simpson’s research is providing much-needed tools to support scientific output and reliability, enabling rich discovery and development for 21st century living.