Turning Real-World Problems Into Research Questions: 9 Questions with Salena Torres Ashton, MSDS '24, PhD in Information '25

Throughout the centuries we have seen how access to information shapes cultures on various strata; we also study the effects of information access on dominant and marginalized communities. How we turn real-world problems into research questions, then data, then information, and how we apply the knowledge from this process, truly influences those in our community.

 
Salena Torres Ashton joined the iSchool PhD in Information program, where she'll also earn the MS in Data Science, after a diverse career as a historian and math teacher. A first-generation student, she holds a Bachelor of Arts in History from Brigham Young University. At the University of Arizona, her research focuses on causal inference, machine learning and knowledge representation.

What brought you to the University of Arizona to study information science?

I only applied to the University of Arizona for my PhD in Information. Of course, there were other programs that I could have attended, but this program had everything I was looking for: diverse department that included math, social history and social science applications.

Tell us about your research focus within the PhD.

I am researching causal inference, machine learning and knowledge representation. It took me four years to narrow my research focus down to causal language and question-asking behaviors that can also infer cause-effect. This research focus addresses what I enjoy learning the most: inferring a person’s goal or intention from their questions, thinking about what goals or concerns people have when they choose to express questions in a particular pattern, formal knowledge representation of these semantics, probabilistic and causal inference, and application in fields like teaching math/programming, family and social history, and addressing academic integrity concerns.

My interest in question-asking behaviors stems from 25+ years of professional family history research and working with clients/patrons/students who would ask questions in such a way that I knew what their goals or intentions were for their research. Sometimes people were hesitant to clearly state their goals because they were hiding family skeletons, they didn’t know the answers and were bashful about that, or they felt inadequate to ask for more help (and would not ask for the help the really needed in their research).

I also taught math at the secondary school level and the way students would ask their questions often informed me of their experiences and attitudes about math. Questions like “So what?” or “Who cares about algebra?!” told me a lot more so I could find different ways to reach students. Question-asking is a way to address asymmetrical information access, information retrieval or belief systems. Question-asking infers a cause or a motivation in some cases, and in other cases it infers an effect or explanation. Of course, this all assumes that questions are cooperative in nature for all people involved in the dialogue!

What do you like best about the doctoral program?

Information science is the perfect union of history, mathematics, programming, causal inference, formalizing language, teaching and logic. The tools and ideas we create in this field help a variety of people in academia, industry and community. Throughout the centuries we have seen how access to information shapes cultures on various strata; we also study the effects of information access on dominant and marginalized communities. How we turn real-world problems into research questions, then data, then information, and how we apply the knowledge from this process, truly influences those in our community.

What has been your biggest challenge in the PhD program, and how have you overcome that challenge?

Learning how to code was my biggest challenge for the first year. I had only two semesters of introductory coding (Java) when I applied to the program. Even though I had 25+ years of work and teaching experience, it was in secondary math and social history research. When I applied to the PhD program, I had several semesters of calculus and linear algebra, but the coding was so new to me. Clayton Morrison, my PhD advisor, and Adarsh Pyarelal, my principal investigator from the ToMCAT grant, not only taught me how to teach myself coding, they guided me on best practices in coding, workflows and collaborating with others on projects that rely on code.

What has been your biggest challenge outside of the PhD program?

The pandemic was difficult for our family because I lost four family members in one year. Furthermore, I wear hearing aids and that made live online classes nearly impossible for me to attend. I was unable to take any math or computer programming courses because they were offered live online. I couldn’t read lips and the transcriptions were unreliable during the first half of the pandemic. I was unable to keep up with what amounted to five different conversations at once: reading lips, reading closed captioning, reading slides, hearing people’s words and having the technical devices required for hearing with clarity.

So for the first two years I had to take courses that were reading-heavy and did not require I attend live online and read slides/code/symbols. When we went back to in-person classes, I was able to take these courses without hearing problems.
 

Write all of your grad school plans on a piece of paper so you have a list of all that is going to change during your time in the program. If you are okay with that kind of change, then you have the fortitude required for graduate school and the workforce.

 
Tell us about your career before the PhD, and what you hope to do after earning your degree.

Information science is my second career. My first career had been 25+ years as a professional historian, specializing in Hispanic family history research methodology. I specialized in Southwestern U.S. and Northern Mexican migration patterns for the 19th century. I also taught secondary school mathematics. My favorite experience for this had been remedial eighth grade algebra because it was 90% discipline and 10% math. That was a result of these students being told the wrong messages about their abilities to learn math. By the end of that semester, we were spending our time doing mostly math, some discipline and some chatting.

My current and future career aspirations are to teach undergraduate courses at a university or community college. I would like to teach mathematics, coding, data science and machine learning.

What student organizations or other extracurricular activities are you involved with?

I am a mentor for Women in STEM and engineering. I also am a certified Data Carpentries Instructor. This year I am serving as the senior data science ambassador for the iSchool. Last year I served for the College of Social and Behavioral Science. 

Outside of school, what are your passions or hobbies?

Cooking, sustainable backyard gardening and solo backpacking.

What advice do you have for prospective iSchool doctoral students?

Write all of your grad school plans on a piece of paper so you have a list of all that is going to change during your time in the program. If you are okay with that kind of change, then you have the fortitude required for graduate school and the workforce. It is a sign of intelligence and tenacity to spend time on a research problem and have negative results (when you can clearly prove what isn’t true). When you are comfortable telling other people, “I don’t know, and I’d like to learn more about it,” you demonstrate an encouraging motivation to learn. You also create an environment that tells others it is safe to ask questions!

My biggest piece of advice: find your people. Find faculty, staff, cohorts, friends and family who will support your dreams, who celebrate your first Haskell program with you, who listen to you talk endlessly about your formal semantic equation derivation of a sentence like “Most dogs swim” (not because they care about the equations, but because they believe in you), and who will scrape you off the ground when you are ready to give up. Find your people who enable you to talk about your code or equations out loud, even if they don’t work in the same field. If you can explain your problem to them, then you finally understand your problem! Find your people.
 

Learn more about the iSchool's MS in Data Science and PhD in Information programs, or explore ways you may support iSchool students like Selena who are driving the digital revolution.