Linda Szabo is a PhD candidate in Biomedical Informatics, advised by Julia Salzman. For her thesis project, she is developing algorithms to accurately detect novel RNA isoforms from high-throughput RNA Sequencing data. Linda received her BS in Symbolic Systems from Stanford and her MS in Cell and Molecular Biology from San Francisco State University. Before returning to Stanford, she worked as a software engineer for several Bay Area start-ups.
This content has been transcribed from an interview that took place on Stanford campus Friday, October 30, 2015 with CEHG’s Director of Programs, Cody Montana Sam and Communications Manager, Katie M. Kanagawa.
Can you tell us a bit about yourself and how you first became interested in science?
I grew up in a small town in central California, called Lindsay. In high school we had one science teacher who taught biology, chemistry, and physics, and he also happened to be my swim coach. I had a good bond with him, so that was always my favorite subject. I knew I loved science but didn’t really have a grand vision for my future. In my graduating class, out of 100, four of us went to four-year colleges, and nobody in my family had gone to college, so it wasn’t really something we talked much about.
I started out at Stanford as a Biology undergrad but didn’t really know what I would do with that. I’m a very active, outdoorsy person, so I think in the back of my mind, I thought I could be a field researcher. I took my first C.S. class as a sophomore to get that requirement out of the way, but the power of writing code really sucked me in. I know there are plenty of people who are really motivated to develop faster, more elegant algorithms. But what motivates me is the ability to write code to solve a real world problem.
I ended up switching to Human Computer Interaction in the Symbolic Systems program. And then after I graduated, I worked at several startup companies in the Bay Area. The first was as an Information Architect, designing web applications at a small consulting firm. I really liked what I was doing. Our customers were companies either trying to design a consumer-facing product or a product for internal use by their employees to improve efficiency and such. Each project lasted about three months or so; we’d go in, go in depth into a problem, and provide a functional prototype solution that we would hand off to their developers to build. I loved that, getting really in-depth on a new topic and then, right around the time that you’re starting to get tired of the nitty gritty details, then jumping into a new area and, again, starting from scratch. Through that experience, I realized that I really liked writing the code to develop the functional prototypes; it’s a really powerful way to express your ideas.
After that I was a developer at a couple of different startups. But then that thought, I used to like science, started popping in. So I took a couple years off to get my Masters in Cell Molecular Biology at San Francisco State. What I realized in that context is that what I can contribute that others can’t is going to come from the informatics side and I wanted the opportunity to further develop that skillset, so I applied to graduate school Ph.D. programs. I’m really excited by the opportunity to tie biology and computer science together and also build in the statistics component that is important in bringing the two together.
Can you tell us a bit more about your current research?
Sure. I’m working with Julia Salzman. In general, we are interested in being able to identify novel RNA isoforms, whether circular or linear. A lot of our wet lab work has focused on circular RNA, but, from the computational side, our broad goal is to develop tools for identifying novel isoforms to get a better picture of what RNAs are out there.
Can you elaborate on the gaps in the central dogma your work on RNA is filling in?
My work is really on the discovery level of identifying new types of RNA. We know now that RNA doesn’t just code for protein. RNA has this whole role of doing its own things in the cell, but we still don’t have a complete picture. We are trying to develop tools to get at that big picture, because we know very well that looking at any one thing in isolation doesn’t give you the whole picture. I’m trying to develop tools that will allow people to discover what RNAs are there and that will lead to hypothesis-driven research.
It sounds like you have often asked yourself the question, “Why am I here?” Do you have advice for other grad students and postdocs for how they can go about answering that question? How do you know you’re on the right track?
I felt a lot of pressure as a first-year coming in, because I didn’t have a grand vision of what I wanted to do. And I found out, talking to people, that many people didn’t either. So I would say:
Don’t be afraid that you don’t know where you want to be, even next year. Just embrace the fact that you’re here and you’re in an environment where you’re learning and you’re able to contribute to amazing science.
But always keep your eyes open so that you don’t miss that opportunity that is going to lead you in to the next phase.
Is there one person who has had the biggest impact on your career?
My husband has pushed me to follow through with the things I speculated about. All these changing careers came in the context of having a very young family and decisions affecting other people. He was very good at listening and gauging my motivation and saying this is something I really support and I want you to be able to do this. There were moments of self-doubt, where I would think this is clearly stressing the family, I probably never should have done this. But he never let me be negative about that. He always kept me focused on the end goal. I never would have gone back to school without him.