Two Penn State statisticians search for the story behind the numbers.
Matthew Reimherr and Ben Shaby. Credit: Nate Follmer, Penn State.
It is used in nearly every field of research, is vital to business and industry, guides critical decisions in government, enables breakthroughs in medicine, and informs practically everything from agriculture to weather forecasting. In this modern era of omnipresent and progressively more-sophisticated technology, it has been invaluable in parsing the resultant data deluge. But despite its tremendous impact, statistics remains one of science’s unsung heroes.
Reimherr found a solution in the Advanced CyberInfrastructure of Penn State’s Institute for CyberScience (ICS–ACI). With more than 23,000 computing cores and 20 petabytes—20 million gigabytes—of encrypted storage on a state-of-the-art high-speed network, the ICS–ACI has the computational power to analyze massive datasets and run sophisticated simulations, and its cybersecurity meets both Penn State’s and the National Institutes of Health’s stringent requirements for the human-subject research data Reimherr works with. Many other researchers at Penn State—in fields ranging from evolutionary biology to astronomy and astrophysics—use the ICS–ACI to perform computationally intensive tasks up to several thousand times faster than on the most powerful desktop system, where they could take weeks or even months to run. One of Reimherr’s colleagues, Ben Shaby, also uses the ICS–ACI for complex statistical modeling, but with applications that are practically a world apart from those of Reimherr’s work.
Shaby works with civil engineers, meteorologists, and atmospheric scientists to model the spacial structure of rare, extreme weather events. "For example," he says, "if you're a city planner building coastal defenses or bridges, you might care about extreme precipitation because of flood risk. And one thing you might want to know is what's the highest-intensity storm you're likely to see. But knowing about such an event in a single location doesn't really tell you all you need to know. What you really need to know is how much rain is going to fall in a drainage basin or a catchment."
To accurately describe such events, Shaby combines a number of probability models that collectively approximate the larger scenario and then, through a statistical process known as decomposition, renders that composite as a hierarchical series of smaller models that represent the underlying patterns—altogether a highly complex set of operations that he, like Reimherr, relies on the ICS–ACI to accomplish.
Despite the striking contrast in the scientific questions they seek to answer, at a high level Shaby and Reimherr have very similar goals and perspectives—both apply probability-based models to make population-level inferences from data—but the specific methods and statistical tools they use are on nearly opposite ends of the spectrum. One practically has to wonder how such an apparent paradox, of two scientist-colleagues simultaneously so alike and so different, came to be.
Matthew Reimherr is an assistant professor of statistics at Penn State.
Ben Shaby is an assistant professor of statistics and a member of the Institute for CyberScience (ICS) at Penn State.