Dan Tierney Biology Meets Technology


Dan Tierney is no stranger to big data. When Tierney founded a financial technology firm in the late 1990s, long before he joined the Salk Institute’s Board of Trustees, he was fascinated by emerging computational approaches that could crunch data and reveal hidden truths.

“We didn’t use the term big data at the time,” says Tierney. “We didn’t really even know what it was. We just knew financial markets are an area that produce enormous amounts of information, and we wanted to know how best to utilize it.”

Fast forward 20 years and big data permeates virtually every industry, including the life sciences. Seeing the parallels with his own experience, and recognizing Salk’s innate ability to incorporate new technologies, Tierney began exploring how machine learning could advance biology. He wanted to see how the lessons his team learned in financial technology could be applied to cells and molecules.

“I’ve had this thesis, for a long time now, that biology and technology are converging and this is one of the most important trends of our time,” he says.

Tierney was keenly interested in how Salk was adapting, and had the resources to offer support. One of his colleagues, Fred Monroe, is a whiz at applying machine learning to financial markets. At Tierney’s request, he is helping Salk researchers harness machine learning to solve problems, such as how to make ordinary lab microscopes perform like their million-dollar cousins.

Tierney has also been working with scientists at UC San Diego to bring Salk into the Pacific Research Platform, a high-capacity data freeway system that encourages collaboration among multiple institutions.

This is the vision Tierney shares with President Rusty Gage, Senior Chief Science Officer Martin Hetzer and many others at Salk. Machine learning and other advanced computational methods can be the secret sauce that accelerates innovation. It’s not if but when.

“The next challenge is to think through, in a systematic way, how Salk can be at the forefront of these technologies, building up internal machine learning resources and tapping into outside resources remotely so we get the best of both,” he says.

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