# Faculty Spotlight

Sean Lawley, assistant professor of mathematics at the U, believes the most interesting math often comes from trying to explain phenomena in other fields. For example, if you’re seeking an answer to a question about biology, physics, or economics, the answer often leads to new and interesting mathematical theories.

“Historically, much of the inspiration for mathematics has come from physics,” said Lawley, “but biology is increasingly a driving force that is pushing the frontiers of math.”

In addition to mathematical biology, Lawley is interested in the theory of probability and stochastic processes, and dynamical systems. Stochastic processes involve systems that change in time and also involve chance.

“Anyone who follows the stock market, gas prices, or the cost of certain grocery items is familiar with a stochastic process,” said Lawley, “because stocks go up or down, and gas prices can fluctuate quite a bit year over year.” Often the same math that describes the fluctuations of gas prices can also describe various biological systems.

On January 31, Lawley will present a free ** Science Night Live** lecture that is open to the public called “Paradoxes, Surprises, and Mistakes in
Probability.” The talk will be held at the Sky Lounge, 149 Pierpont Avenue, in Salt
Lake City, with a pre-lecture social at 5:30 p.m.

Lawley will discuss how probability is often counterintuitive. “In probability, what we naturally think is true often proves to be false upon closer examination. In my talk, I’m hoping to showcase a few examples to illustrate and correct where our intuition goes awry.”

“I’ve always been interested in math,” said Lawley. “As a child, I liked patterns, and then, as I got older, I enjoyed the precision, rigor, and universal aspects of math. I’m grateful to my parents, who encouraged my studies.”

Lawley obtained his undergraduate degree from Carnegie Mellon and his doctorate from Duke University. He joined the U as a postdoc in 2014 and became an assistant professor in 2016.

“It’s truly a wonderful time to work in stochastic processes,” said Lawley. “Advances in experimental techniques now allow researchers to track the paths of individual molecules with very high resolution. Some of the new statistical data show significant deviations from classical stochastic process theory from more than 100 years ago. The challenge now is to develop mathematical theory to explain these novel statistical features.”