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Julia Olivieri

PhD candidate, Institute for Computational & Mathematical Engineering
I never used to think of myself as someone who’d study computer science, and I didn’t think of becoming an engineer until graduate school.

I didn’t even consider any CS classes in high school, because it seemed like the people taking them already knew a lot about computer science, and it was intimidating. I thought people like that were men who were very into video games and very self-motivated to do things on the computer. I eventually went to a small liberal arts college that didn’t have any engineering; I enjoyed math and science, but I also really liked English and history, and at one point considered majoring in English. I just followed my interests.

Things changed when I finally took an introductory course in computer science – the only CS course I took in college – along with courses in discrete math and biology. Soon after, I attended a conference for undergraduate women in mathematics that was filled with women with PhDs and female math professors. It made me feel like there was another world I hadn’t considered, and that a career in math and academia was achievable. Later, I spent a couple of summers doing research where I learned about computational biology, which joined my math and biology interests. It was exciting, and also taught me that I liked the computational side of research.

Right now I’m the co-president of Women in Mathematics, Statistics and Computational Engineering (WiMSCE) here at Stanford, and I work in the Salzman Lab, where I study individual cells that carry different versions of the same gene to understand how they differ from each other. The body is able to splice genes to create many variants of the same gene, and these variants can have very different functions and be implicated in cancers and disease. We now have the ability to see this process, and can get data from millions of individual cells, but to draw any conclusions from that data we need to use advanced computational methods. That’s what I focus on. In the future I think we’ll be able to use this information to learn not only how to more effectively treat disease and better understand how the human body works, but to understand what is shared biologically across the whole tree of life.

Next fall I’ll be an assistant professor of computer science at the University of the Pacific. I may not always have thought of myself as an engineer, in part because I used to associate it with doing something more physical than computation. But today I do, because I’m focused on practical solutions to real problems, and I can see that I’m starting to make a dent in understanding how the human body and its trillions of cells actually works.

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