When it comes to bringing beauty to big data, University of Utah computer science professor Miriah Meyer is a rock star. Her work gives life to massive sets of data points, making research and findings easier to understand for both the layperson and those directly involved with the information, like biologists, doctors and geneticists.
She’s an aesthetics specialist, an info-graphic guru, a visualization magician who has been featured as one of the “most creative people” by Fast Company magazine, highlighted by MIT Technology Review as one of the “35 innovators under 35″, and named a fellow by TED, PopTech and Microsoft.
According to Meyer’s online bio, her work “allows scientists to validate their computational models, to understand their underlying data in detail, and to develop new hypotheses and insights.” Frankly stated: she gives clarity to some very complex stuff.
The components that go into delivering such clarity can be pretty complex, too. Meyer first collaborates with the experts from whom the data is derived to determine how best to build and incorporate computational geometry and computer graphic algorithms that power the visualization. She also must examine the subject from a more artful angle, leaning on the principles of design, layout and user experience to make information accessible and memorable.
“As a global community, we have tons of data, whether medical, financial, or from scientific devices. So, all this data, underneath it’s just a bunch of numbers,” Meyer told the Harvard School of Engineering and Applied Sciences, where she spent time as a postdoc. “As humans, our brains are like a computer with limited memory. So we can rely on the outside world as an external hard drive. We can store all this information in diagrams or graphs or whatever. In visualization, we rely on our perceptual system to be able to see patterns and see trends.”
One project that exemplifies how Meyer’s work can help scientists see the layers of information of given research is “Pathline”, an interactive tool that conveys temporal gene expression data over multiple molecular pathways across multiple species with the ability to be charted in various ways. It’s this ability to see the relationship between the moving parts that helps researchers identify how the different variables react with each other.
It’s all work that Meyer hopes will help catalyze the next big breakthrough in fields as diverse as astronomy to biology, or wherever big data needs to be digested.