This week on Theory and Practice, we speak with Jennifer Listgarten, a professor in UC Berkeley's Electrical Engineering and Computer Sciences department and the Center for Computational Biology. Before Jennifer joined UC Berkeley she spent ten years at Microsoft, working in the New England Research Lab.

One particular point in our conversation sums up Jennifer's life's work. We talked about how tackling a problem in biology often requires the development of an entirely new machine learning (ML) technique. "It's very, very hard to find a problem that matters, where current tools aren't good enough to do something very useful, and where the development of new tools really make a difference," she says. "If you can do that, you're smart enough to do everything else because you have to sniff around, I think, and have a bit of a gut feeling."

Jennifer's superpower is finding those biological questions for which our current ML tools don't work. This feeds into a theme in our show: matching problems to solutions between the life sciences and data sciences. In a very good way, Jennifer is a bit of a maverick. She's not afraid to go against the grain and break into a new territory, similar to Professor Rory Collins who joined us last week. Both were fascinating to talk to because they are willing to break conventional norms when they have conviction in their beliefs.