The Paths to HPC series, presented in collaboration with Women in HPC, showcases the 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?
My path was definitely an indirect one. I am a historical sociologist by training. While in graduate school, one of my mentors had just received a faculty fellowship with the National Center for Supercomputing Applications (NCSA) on UIUC's campus. She was looking to acquire a large dataset via HathiTrust that required massive computing capacity to analyze, and wanted me to join the team because of my specific expertise in Black Feminisms.
I was so reluctant to join, I remember saying "I don't know anything about supercomputers! I like being in the archives with dusty boxes. This is not my speed." The project was intriguing though, because it was attempting to use computation to identify and retrieve Black women's voices from a massive digitized historical record.
It was also during a time when I was becoming particularly disillusioned with the limitations of methods within my own discipline. Having experienced the violence of physical archives and their erasure of Black women's experiences, I was really curious about the potential of computation to get us closer to Black women's experiences when they could be found, and when we couldn't get closer, how computation might help us understand some processes by which Black women are erased or rendered invisible. The questions haunted me.
In high performance computing, everything seems possible. And being grounded in Black feminist praxis gifted me the discernment to question what we should be doing, and what we shouldn't be doing, when everything seems possible.
After I completed my PhD, I was awarded a postdoc at NCSA, where I continued this work of theorizing about methods, and playing with computation, simultaneously embracing and rejecting the idea of 'big data,' and cultivating a more critical perspective of how computation served to liberate and oppress.
What’s cool about working with HPC?
I really enjoy using the tools in ways they were not originally intended. As examples, during my postdoc, I worked with amazing visualization experts to try to visualize intersecting identities within corpora. I created a methodology that coupled Black feminism and computation. I also merged statistical modeling with autoethnography.
I love the creativity HPC encourages. I had spent a good amount of time studying social media platforms, and they served as a seemingly unending source of information. I guess I was intrigued by the interdisciplinary possibilities. In high performance computing, everything seems possible. And being grounded in Black feminist praxis gifted me the discernment to question what we should be doing, and what we shouldn't be doing, when everything seems possible.
What are some of the challenges you have faced in taking this path?
There's the constant imposter syndrome. Though I am married to a computer scientist, that knowledge doesn't directly translate! There was—and is—a huge learning curve. I often found language to be a huge stumbling block.
I sometimes feel that I am illegible in these spaces. But that's also what makes my work fun! Sometimes, my illegibility serves as a superpower.
Rather than respecting what each of our disciplinary approaches brings to the table, and coming together to not only translate for each other but also create a new language via a reciprocal process, I often found that the learning seemed to be one-sided: that I was the one who needed to get up to speed on the technical languages and processes. I am convinced that ALL computer scientists need to learn about Black feminism(s), or some critical theoretical frame to challenge their disciplinary assumptions.
That has been a real challenge, because it's not just a challenge of learning to elegantly code, or having a deep understanding of statistics, it's about an epistemological stance that privileges certain types of knowledges over others, often quantitative over qualitative, and efficient and simplistic over complex and messy. I sometimes feel that I am illegible in these spaces. But that's also what makes my work fun! Sometimes, my illegibility serves as a superpower.
Are there any mentors you would like to thank?
I am absolutely indebted to Dr. Ruby Mendenhall for encouraging me to enter into this space, encouraging me as I grumbled about 'not knowing anything about supercomputers,' and for leading by example to show me that, yes, Black women from disciplines outside of CS belong in these spaces too. I appreciate her radical imagination and big vision. She definitely nurtured a curiosity in me that has been lifegiving.