Tell me about your background and where you grew up.
I grew up in the Midwest. My family is from St. Louis, and I primarily grew up in Iowa. After high-school, I spent about 10 years in Minneapolis and consider it my hometown. I grew up with cerebral palsy and often found online spaces easier to interact with others. This may account for my career trajectory, in-part. I now work in machine learning research. In my free time I’m very passionate about music, and competitive video games (one-handed).
How did you become involved in Computer science?
I didn’t particularly excel in high school, and didn’t have many aspirations after graduation. Honestly, I skipped a lot of school in favor of doing general tech/online stuff at home. Lacking other ideas, I joined a high school friend at a Minneapolis tech school in “coding.” Professionally, this wasn’t a great resource. A few years later I applied to (only) the University of Minnesota and was accepted provisionally, since my college prep was lacking. My math level was three courses before Calculus I, and I was two years of prerequisites before starting my CS curriculum. In CS, I could “code” procedurally, but I first appreciated computational thinking with functional programming in LISP (Structure and Interpretation of Computer Programs, Abelson and Sussman). It’s a coincidence of the Minnesota CS curriculum that I was exposed to this very different paradigm, to recursion and algorithms within this first course. I gravitated to the math (vs. engineering) side of the undergraduate curriculum, and continued in discrete math, complexity, graph theory, and algorithms.
From there, I continued to a M.S. in Computer Science (Minnesota) and a PhD in Computer Science (University of Illinois at Chicago). So, I suppose I’m a computer scientist by now.
What are the key projects you are working on today?
I recently defended my PhD at the University of Illinois at Chicago after accepting a full-time research scientist role at Salesforce.
My PhD focuses on methodologies for inferring graphs (e.g. social networks) from data. What we find is that applications have a great deal of freedom in the definition of these networks, and this definition has a huge impact on the downstream machine learning models.
My work at Salesforce was primarily in the area of fairness and bias. We worked with customers in novel application areas which do not fit under the current work of individual or group-wise fairness. I’m very interested in fairness in structured environments such as social networks and urban spaces.
Your panel is focused on the impact the Tapia Conference has had on your professional life. Tell me about your first experience with Tapia.
My first experience at Tapia was 2017 in Atlanta with the support of AccessComputing at the University of Washington.
I’ve made a lot of friends over the years in the wider “broadening participation” communities, from my involvement in the Broadening Participation in Data Mining workshop at the KDD conference and other diversity programs. For me, I don’t immediately identify with a “birds of a feather” disabled identity. In part, our experiences and needs are very different, and second, I’ve spent most of my life minimizing special attention to my disability and/or refusing accommodation.
My first experience at Tapia 2017 in Atlanta inspired me to attend every year from then. I feel affinity with many students of underrepresented backgrounds, especially non-traditional students. To me, the greatest impact of Tapia is being able to directly mentor and support junior students. This was the first conference that I was conscious of my role and value, as a senior to undergraduate or graduate students.
Why should students attend the Tapia Conference this year?
Students should attend Tapia because the conference is designed to directly support them. Students have a great opportunity to meet other students of similar backgrounds, make friends, and have fun. But mentors and exhibitors are there to directly support the careers of students and have had a great impact on countless attendees.
Aside from students of underrepresented backgrounds and sympathetic allies, we need more attendees who haven’t given diversity in computing much thought. In my experience, many research groups are focused on the academic paper pipeline and don’t heavily value diversity mentoring except with social praise. This creates a like-minded “diversity community” where outsiders don’t see the career value. Students (and departments) should invite these non-inclined faculty (e.g. passively “agreeable”) to experience Tapia for the first time and more actively participate.