In 2013 David Christle was a PhD student at University of California, Santa Barbara when his professor, David Awschalom, announced that they were packing up the lab and moving to the University of Chicago.
They were told they would be part of a new collaborative school called the Institute for Molecular Engineering, and that they’d be setting up temporary lab space in a basement until the new lab was ready.
“I was a little shell-shocked,” said Christle, PhD’16. “We had to move all of our experiments across the country and set everything up right—the alignment of the optics, everything. But we had a hand in how the labs would be set up and designed, and it was fun because everyone else was in the same situation.”
The move turned out to be a good one. At what would become the Pritzker School of Molecular Engineering, Christle found himself part of a new group of graduate students from various disciplines who all hung out in the same building. “It made spontaneous conversation easier, which led to new scientific collaborations for many people,” he said.
Christle studied silicon carbide for its potential as a new kind of qubit for quantum computing and communication applications. “We had a lot of autonomy to pursue what we wanted to pursue,” Christle said. “David was incredibly supportive of that. Now that I’ve left, I understand that was rare.”
He spent many late nights at the lab listening to songs like “Want U” by Grum. “Being in the lab at midnight, with all the lights off—it’s an optics lab—getting single defect measurements dialed in for the first time with some newly written code, sample or optics, and hitting the ‘execute’ button with this song blasting felt sublime,” he said.
Even among quantum engineers, Christle was known around the lab as the data guy, with a penchant for the stories that numbers could tell. After a postdoc in the Awschalom Group, he landed a data science fellowship. That led to a job with LinkedIn, where he used machine learning to protect LinkedIn members from activity by bad actors. He has since moved on to be a machine learning engineer at Cash App.
Though he ultimately chose a career outside of research, the lessons he learned at UChicago still resonate.
“With problems in tech, there is no textbook,” he said. “It’s more like, here’s a problem and we don’t have any solutions. You need to learn how to take a scientific approach to thinking about it. You need to be comfortable with unsolved problems that are difficult, and you need to have ‘data skepticism,’ learning how to find the true signals in the noise. Working in a lab and failing a lot is how you learn to do that.”