Story by Eric Geringas
FROM AN EARLY AGE, Jielai Zhang’s life has been governed by two powerful forces. One is an insatiable curiosity, which, in high school, caused her to fall in love with physics.
“Ever since I was little I asked lots of questions,” she says. “Like, where did that plant come from? Where did the seed come from? Where did the Earth come from? And I realized that a huge part of physics was to try and explain how things work.”
Ten years later, Zhang had two bachelor’s degrees from the University of Sydney in Australia—in physics and math, and in aerospace engineering.
And the other force? It’s a love of gambling, which she says she inherited from her parents, engineers who moved their family from China to Australia when Zhang was young. That interest came to the fore when it was time to choose a research topic for her PhD.
“I like projects where there’s a huge element of uncertainty,” she says, “combined with a huge potential payoff.”
Zhang had already decided to head to Toronto—attracted by the Dunlap Institute for Astronomy & Astrophysics, which specializes in instrumentation, and the Canadian Institute for Theoretical Astrophysics, a nationally supported research centre that is based at the University of Toronto. “I thought if you wanted to do astronomy you shouldn’t focus on just observations, or instrumentation, or theory. You should be able to link these things together. And the University of Toronto is one of the best places in the world where that is all together in one place.”
She wanted to look for answers to one of the toughest questions she had asked as a child: “Where do galaxies come from?” And at the University of Toronto she chose the riskiest project she could find: Professor Roberto Abraham’s then-brand-new Dragonfly telescope, an outside-the-box venture that uses an array of commercially available telephoto lenses to peer into the darkest corners of the universe.
Zhang spent five years working on data from Dragonfly, studying the evolution of galaxies—something she calls “a tantalizingly complex field that requires an understanding of physics on the largest and smallest scales”—supported in part by the Carl Reinhardt Fellowship in Astronomy and the Shirley Jones Fellowship.
When she was close to finishing her PhD, her love of risk popped up again. Zhang heard about a generous new post-doctoral fellowship with an unusual requirement: it was going to fund young scientists to work in a field outside their normal area of expertise.
So she doubled her bet: she conceived a project that was as far from her comfort zone as she could get. She proposed using imaging techniques from astronomy to study the human body and try to pinpoint the origins of disease.
“I’d been thinking a lot about what other directions I could take my research,” she says. “One reason I absolutely loved my PhD project was that I was able to work on the development of the Dragonfly telescope, and the capability to image something we’d never been able to image before.”
That capability presented an opportunity. Astronomers collect vast amounts of data on celestial bodies, and use machine learning to sift through it for useful information. Zhang wanted to know if this methodology could be applied just as effectively to a much smaller object of study.
“How do you detect when something has changed? There’s a lot of research going on to detect that difference automatically,” Zhang says. “That got me thinking about the diagnosis of disease, where the image you take—for example in MRI—looks different from what it’s supposed to look like.
“Everybody knows that diagnosis is difficult and that a doctor needs a lot of training. But the information must be there in the data, so there must be a way to get it out.”
And that bold idea was what won her a Schmidt Fellowship, one of only 14 in the world—a new award endowed by Eric Schmidt, one of the founders of Google, and administered by the Rhodes Trust.
Armed with funding of $100,000 US, Zhang will spend the next year trying to find a way to make her gamble pay off.“
There are a lot of common challenges, and probably a lot of common techniques, so my skills are useful,” she says. “But it’s a risk, because I don’t know about brains. I don’t know about medicine and health. And I can’t be 100 percent sure that I will be useful to the medical profession since I haven’t tried it before. But…it’s so exciting!”
TOP: Jielai Zhang.
BOTTOM: The Dragonfly team. Image courtesy of Roberto Abraham.