Unlocking Powerful Tools
Neva was aware of how technology had changed the landscape in her field, but she lacked the knowledge to fully participate in this brave new world.
“I knew there were people at the UW doing machine learning, and I could read papers about it, but I didn't speak the language of machine learning and data science,” Neva says. “They have their own vocabulary, and I didn't understand it. There are also common algorithms published in my field, but I didn't know how they worked.”
What she learned in the UW Certificate in Data Science was truly transformative, she says. The knowledge gave her a new perspective, reinvigorating her passion for the work and her excitement in what was possible.
“As I began studying data science, I realized I absolutely love it,” Neva says. “I love the material. I love coding in Python, and I love the machine learning toolboxes that are available. Not only are these tools that will be valuable in my job, but they’re also something I really enjoy utilizing.”
Making Valuable Connections
Armed with the fresh knowledge she gained in the certificate program, Neva began exploring how to apply it in her day-to-day work at the UW Institute for Learning & Brain Sciences. It didn’t take her long to find a way, an innovation that would pay big dividends.
Before taking the certificate program, I would never have contacted Ariel, and he’s become a mentor to me. There's a whole language that goes along with machine learning, and I wouldn't have understood how to implement it without those courses.
— Neva Corrigan, Alumna, UW Certificate in Data Science
“I was working on a longitudinal study of the adolescent brain that was interrupted by the pandemic,” Neva explains. “When my manager asked me if we could use the data to look at how the pandemic affected the brain, I told her ‘no,’ because we didn’t have a control group.”
In need of help, Neva reached out to Ariel Rokem, a machine learning expert and researcher with the UW Department of Psychology and the UW eScience Institute. Together, they came up with a way to use the study data that Neva already had to generate new findings.
“He introduced me to normative modeling,” Neva says. “By using this more advanced modeling, instead of traditional statistical models, we were able to examine the effect of the pandemic lockdowns on the teenage brain.”
According to Neva, it’s the kind of collaboration that wouldn’t have been possible without her experience in the UW Certificate in Data Science program.
“Before taking the certificate program, I would never have contacted him, and he’s become a mentor to me,” she says. “There's a whole language that goes along with machine learning, and I wouldn't have understood how to implement what he was talking about without those courses.”
Impacting the Conversation
Through this advanced data modeling, Neva and her colleagues found that the COVID-related lockdowns resulted in accelerated brain maturation in adolescents. The changes were especially pronounced in girls, who had a mean acceleration of 4.2 years versus 1.4 years in boys. These findings support the idea that the pandemic negatively impacted the mental health of teens.
It was a thrilling moment, Neva says now.
“For all the years I’ve been studying the brain here at the UW, it's seldom that you come across a finding that is momentous enough for the general public to pay attention and say ‘wow,’” she says. “I’ve published tons of papers, but I haven't previously had a study that has such tangible results that are so relevant to an experience that everyone identifies with. It's been fantastic.”
When the researchers presented their findings at the annual Society for Neuroscience conference in November 2023, a number of national publications covered the news. Nearly a year later, the work was officially published by the Proceedings of the National Academy of Sciences. And Neva and her colleagues are uncovering even more findings as they continue to explore the data using these powerful tools.
Charting a New Course
The consequence of learning these new skills goes beyond individual research projects or papers, however. It’s given Neva a fresh outlook on her career.
I fell in love with MRI back in grad school, and now I’m falling in love with data science. This is what I want to be doing, and I have a passion for it.
— Neva Corrigan, Aluma, UW Certificate in Data Science
“Since I started taking courses with UWPCE, I’ve switched to programming in Python, which was required in the courses because that’s what most common data science packages use,” she explains. “I fell in love with MRI back in grad school, and now I’m falling in love with data science. This is what I want to be doing, and I have a passion for it.”
Her experience with the data science program was so positive that Neva decided to follow it up with the UW Certificate in Machine Learning. The more advanced program has allowed her to deepen her knowledge and sharpen her skills.
“In this program we're hand-coding a lot of the mathematical formulas available in machine learning toolboxes, writing them from scratch so we know exactly what they’re doing,” she says. “We're using algorithms that I've heard about but never really understood in the past. After this course, I think I’ll have a good foundation for implementing these algorithms with many types of data.”
As for her future career trajectory, Neva isn’t totally sure. But she remains excited about the new pathway her UWPCE experience has enabled.
“I see this huge need in the UW community for data science expertise. I’d really love to be someone people can call on to help them analyze their research data,” she says. “UW has a new Institute for Medical Data Science, and it would be wonderful to be a part of that someday.”
