From expectations of becoming a farmer to the ground floor of US supercomputing
Guest post by: Alisa Alering, Science Node
First published on Science Node on 21st January 2020
Our new series, Paths to HPC, showcases women working in high-performance computing. Our hope is that by highlighting these trailblazers—and the sometimes unique paths they followed into the field—other women will feel inspired to envision themselves in similar roles.
What was your path to working with HPC?
I came of age when my home country reopened the universities to the general public since their closure a decade earlier. While in high school, I took it as a given that I would become a farmer after graduation, as hundreds of thousands of high schoolers did before me. So it was quite astonishing when the doors of college suddenly opened to me. I majored in computer science, although the only knowledge I had about computers at the time was that they were the future.
My initial foray into HPC was not intentional. While at the University of Illinois pursuing my PhD, I was lucky enough to work at the National Center for Supercomputing Applications (NCSA) soon after it was created. Working alongside the leaders of scientific computing was both intriguing and exciting. I spent five years there and thoroughly enjoyed the work, whether it was creating tools to access the Crays or visualizing simulation data until I left to focus on my thesis research.
After getting my PhD, I went to work in the industry for many years, leading software engineering teams to develop products in medical imaging, healthcare information systems, and publishing. When I later found myself in West Lafayette, Indiana, Purdue’s HPC program was expanding rapidly, and it became a new site in the NSF-funded TeraGrid project. I felt like going home – to be able to work on HPC again!
I joined Purdue’s Rosen Center for Advanced Computing as a senior scientist in 2005, had the wonderful opportunities to lead Purdue’s resource provider role in TeraGrid and later its partnership in the XSEDE and the current XSEDE 2.0 projects, thereby completing the circle back to HPC fifteen years after I had started.
What do you like about working with HPC?
When I graduated with my PhD, my advisor had a heart-to-heart with me and asked what I really liked to do. My answer was that I really like the applied side of things—bridging technology with some sort of science application.
Over the years, HPC has become more interdisciplinary. Now, even one project touches many different aspects of science and different people with different backgrounds. That to me is really, really cool because I get to learn a lot of things.
I’m still not a domain scientist, but at least I understand a lot of the problems. Because we need to help scientists share their models and simulation and other analysis tools we need to understand at some level the type of data that they use and how to take its different variables into consideration. I love that closeness to science and things that can impact our lives. That’s the draw.
What are some of the challenges you have faced in following this path?
I used to try to learn everything because I’m curious and I like to learn. But things are moving very fast now and new technologies come around all the time.
It has come to the point that one person cannot possibly know all the technologies that you need for your job. As a result, the work has definitely become more collaborative. Which means soft skills are becoming more important to help you work with people and articulate what you need.
Sustainability is also a big challenge. My group always sees more work that we could be doing, more people we could support, more new ideas we could implement, but without funding, we couldn’t do any of that.
I think gender-wise, I was fortunate in that I grew up not having the concept of “these are fields women aren’t supposed to go into.” There were a lot of bad things happening where I grew up, but one good thing was that equality was really drilled down to people.
Any mentors or role models you would like to thank?
Definitely Mike Norman. I always think back to my days at NCSA and how happy and excited I was to be working with him. In those early days of the US supercomputing program, I was really lucky to work with people like Larry Smarr and Mike Norman and later, when I went to Purdue, Jim Bottum.
These were all science people, but they were really passionate about computation. I had a tremendous time working there. Mike was a great mentor, and we created one of the first scientific visualization tools together. He was always very encouraging and never made me feel like I was a student. I was just one of his colleagues working in the center.
Then there’s my PhD advisor, Professor Jane Liu. She works really hard and she’s still doing research in her 70s. She was one of the very few women in computing in the early days, and she kept going and she never gave up. She was very particular and always insisted I be really thorough with my methods and writing: she literally cut and pasted my very first research paper —with scissors!
She also talked to me about life. That you have to have your own thing. She gave me examples of her friends who had turned out bitter later in life because they threw everything into their kids and family and didn’t have their own career to stand on. I still look back and think she’s one of the great role models for me.
About the author: Alisa Alering, Managing Editor, Science Node
Originally trained as a librarian, Alisa loves tracking down the science behind her stories and learning something new about technology every day. With previous experience as a freelance writer and photo editor, she has held positions at Indiana University Press, PBS, and Google and earned degrees from Penn State and Indiana University. She particularly enjoys writing about women and diversity in technology, digital humanities, and the intersection of science and the natural world.
About Science Node
Science Node is an online magazine that connects the global research community, exploring how tech works and showing why it matters to our everyday lives. sciencenode.org