Oral-History:Fay Cobb Payton

About Fay Cobb Payton

Fay Cobb Payton.jpg

Dr. Fay Cobb Payton is a Full Professor (with Tenure) of Information Technology/Analytics at North Carolina State University and was named a University Faculty Scholar for her leadership in turning research into solutions to society’s most pressing issues.  She is on rotation as a Program Director at the National Science Foundation (NSF) in the Division of Computer and Network Systems.  She is a full member of Sigma Xi,  and serves in the following capacities:

  1. Association of Computing Machinery (ACM) Education Advisory and co-chair of its Diversity, Equity & Inclusion Committee
  2. American Association for the Advancement of Science (AAAS), Science and Technology Fellows Selection Committee
  3. Institute of Industrial & Systems Engineers, Health Systems and Diversity-Equity-Inclusion Committees
  4. American Council on Education, Council of Fellows Leadership Board

Dr. Payton has received the North Carolina Technology Association Tech Educator of the Year, PhD Project Hall of Fame and National Coalition of Women in Information Technology Undergraduate Mentoring Awards.  She is a American Council on Education Leadership Fellow and National Institute of Environmental Health Sciences Fellow. Her research interests include healthcare IT/informatics/disparities; data quality; bias in AI/information seeking/HCI; access and participation in computing/STEM and entrepreneurship pathways. She has published over 100 peer-reviewed journal articles, conference publications and book chapters.  She worked in the tech industry prior to joining the academy.

Dr. Payton describes her experiences working as an industrial and systems engineer in the government, academic, and industrial sectors – and how these sectors differ in terms of speed of action, resources, and funding. Topics include: her upbringing and how she became interested in computing; her work with large data sets from the health and financial industries; and her participation in creating better STEM education, especially for women and minorities. She discusses how it was for a Black woman to receive a technical higher education and be successful in these different sectors, especially given the small number of senior Black or female role models.

Interview

Interviewee: Dr. Fay Cobb Payton

Interviewer: Roli Varma, University of New Mexico, Albuquerque

Date: 14 December 2020

Location: Via Zoom

Varma:

Dear Fay thanks for agreeing for this interview. Currently, you are a program director in the Division of Computer and Network Systems at the National Science Foundation. This is a rotation-based position. You are a Professor of Information Technology/Analytics at North Carolina State University. Prior to joining academia, you worked in the corporate sector. Basically, you have experience in working in all three sectors: government, academia and industry. Can you talk about working in these three different sectors? What opportunities and challenges did you have in these places?

Payton:

So, you want to know about the challenges associated with being in three sectors? These three sectors are very different. I will start with the corporate since that is where I started. I think the challenges associated with corporate, oh gosh, there are many. First, it was finding an environment where you can have some level of autonomy. At that time, it was challenging for me. I also think that I was dealing with the kinds of problem sets that I had were not at the time structured for interdisciplinary play (thinking), that I enjoy doing. It was a very structured environment. I certainly did not see many black women in leadership roles. That was a challenge. I think to some degree, having sponsorship and finding the sponsorship at that time just was a foreign concept to me. Sponsors help usher your career along an upward trajectory, and I did not really have that. I had mentors. In terms of academics, it is isolating; it can be very isolating while finding appropriate shifts in the research, teaching and service model that many of us (particular in tenure track positions). There are challenges associated with being one of very few black women or women of color. Again, I enjoy interdisciplinary problem solving and approaches and teaming with others across different departments. For me, this might be engineering, it might be public health, it might be the school of design. I think at that time, those kinds of models were not quite acceptable. Being on the tenure track when you have an interdisciplinary mindset can have a cautionary tale in the academic space, in academic life. Now, I am in government. So, in government, I think some of the challenges are, how fast do you move? When do you move? How quickly do you move? You need to understand that all institutions are not equal in terms of resources and funding. I think these are some of the challenges, again, still, regardless of whatever environment I am going to still go back to this beyond the technical aspects of what the challenges may be still being, one few black women. And I think that, that those are challenges in and of itself when it comes to inclusion within all three domains.

Varma:

My understanding is that it is very challenging to move from private sector to academia, and added to that is being interdisciplinary. How did you manage to get academic position?

Payton:

That is a great question. You did not tell me it is going to be a difficult question. I think for my time in industry, having resources was less of an issue than it was in academia. Academia seems to be in this constant compete for limited resources. When you layer interdisciplinary thinking in resource constrained environments with siloed disciplines/departments, it is a lot to manage. I have to think how did I manage that? I worked to surround myself with a great team of researchers and colleagues that had similar interests in terms of the types of problems we were looking to explore, study, write about, and investigate. Having a core team was very important to me, as an academic researcher.

Varma:

Could you talk about your technical accomplishments before we move to other things?

Payton:

Actually, before my PhD, my work really began examining large scale data sets for ARIMA (time-series) forecasting models. I stayed on that data quality/integrity trajectory. My work has always had a data focus, and what does data tells you and not tell you about a specific phenomenon in a particular context. In this instance, I started out in my PhD program, looking specifically at healthcare data sets. My goals and perspectives were to examine how we can use financial and clinical data in order to provide health care services via tech innovation that could reach people in places that were otherwise disenfranchised. I help design and assess applications for home care delivery systems, particularly among patients with HIV socially isolated at the time, as well as persons and caregivers. Well, I should say persons living with Alzheimer's disease and their caregivers. We were examining how do you involve patients in the loop, via data and technology infrastructure. The other piece of the data set, particularly in healthcare is looking at co-morbid conditions. In other words, how do diseases cling together, for instance, particular health disparities, and what are the implications that we can draw on cost of care and length of stay for patient populations. In more recent years, I have worked on natural language processing, seeing what is it that we can learn from small data or unstructured data. This works involves asking what is in the data, how is the data structured? what is missing in the data and how fair is the data. So particularly when we get to this, the notion of fairness in AI, particularly looking at what is in the data, because it is not just a data problem. Instead, it is a whole host of other issues around confirmation, around setting up training data, and then coming up with how do you even ask problems around data? Who is designing, asking, excluded, and why small data are critical?

Varma:

So, as a social scientist, we always look at data and we say, what are they telling, and what they are not telling.

Payton:

Exactly. And I think a large part of that recognition is there is so much talk about what is in big data. Rather than ignore small data, pairing it with big data and plus inclusive talent to to tell a story can better address issues of bias (in its many forms).

Varma:

You are deeply involved in STEM education for women and minorities. You are author of Leveraging Intersectionality: Seeing and Not Seeing. So, you do both, technical and educational work. How did you get involved in educational or social aspects?

Payton:

It is more about equitable inclusion and less about education are the kinds of questions that I am asking. So, I am not necessarily asking an education question. I am asking, are we propagating inclusive ecosystems? And then I asked the question if we are being inclusive, what do the data say? So, it is always this question what do the data say and, and what types of data are we using and the associated biases? Your question how did I get involved in this space? I think, it was through my training. I want to say in my doctoral studies, I had technical people that were computer scientists. And, I remember one member of my committee who was also a systems engineer, asking me the questions: did I consider generalizability and how generalizable were my findings? And from that point on, I think I began to ask the question continously. What she was telling me is I need to understand the context and working with her, particularly in a healthcare environment, when you ignore context and the social aspects of data outcomes, you miss the opportunity to make a contribution to populations of people that are experiencing health disparities and chronic diseases. Clearly, we were exploring issues that needed an interruption beyond the problems associated with the data (going beyond the data problem/bias). This is where that comes from. I am constantly questioning what is the context for the finding.

Varma:

Tell me the story of how you became interested in computers. When you became interested? What interested you? Who interested you?

Payton:

Oh, it is a fascinating, it was a fascinating. I did an internship at a national lab when I was in high school, and a part of my summer experience, a part of my internship was to calibrate these employee hazardous explosure measurements from, since at the time sort of low-level sensors, not sensors as we know them today. So, I did the data entry and wrote code for the data analyses. I remember the woman that I reported to said, okay, now that you have done that, now you need to figure out how to write some code to calculate. What was measured? Are the readings within the bell curve? Are there outliers? What are our general statistics on these? Can we say something about a class of workers at the time that were wearing these sorts of sensor? I was absolutely fascinated with honestly with problem solving aspects. I attended a magnet school which was intended for young people who were interested in becoming physicians. We had clinical rotations, and one of my clinical rotations went bust because I just could not stomach what I was seeing in terms of surgeries in the hospital. My teacher noticed that I enjoyed the data aspects of it….the non-clinical aspects of health care. And she put me in front of a computer. I think the link between those two caught my attention. I started out in engineering and, and the engineering aspects of my work forced me to use computing. I could not do my work without computing. The love of problem solving got me hooked.

Varma:

That is nice. Some background questions. Where did you do your high school?

Payton:

I went to high school in Georgia.

Varma:

When you were in elementary or middle school, what did you want to be? Do you remember? How about in high school?

Payton:

I knew that all of my work from elementary school on where there was a big push and focus in mathematics in my home. So, I knew whatever it was going to be, mathematics woud be inherently included. And by the time I reached high school, I knew that I did not want to be in “this” after clinical rotations. I knew….I did not have the stomach to be the medical doctor. I saw live birth when I was 16, and I said, okay, that is it. I am not going to be the OB GYN as originally planned. During a car ride home with my father, he planted a seed. During our discussion, my father talked about my studies and mathematics – then, he dovetailed this with the internship at a national lab. From the point, I was certain that I was going to study engineering. I did not think about being a researcher. I knew that I was really good at math and that is what my teachers, my parents, family, my ecosystem stressed this to me.

Varma:

In your own words, what experience was most responsible for your decision to study computing related field?

Payton:

I would say it was part having the engineering experience. I am an industrial and systems engineer which focuses a great deal on statistical modeling, engineering economics, simulation, process improvement and human factors. All of this required data analyses. And, to analyze that data, you need computing. Parental influence was important for me. My parents pushed me in a way that was STEM focused. My mother has some college education, but my father doesn't. I think that there's a lot in that story that, you know, gets missing. My time in corporate out of undergrad (along with years of co-op and interns) was important. I was working at IBM where everything was about computing power. My corporate training included software design and hardware specification. This combination contributed to my studies in computing.

Varma:

Did you have a role model for your study in college? What people in your life influenced your decision to study in college? How did they influence you?

Payton:

Oh, my mother, my mother is my role model. I know a lot of people are inspired by those in the discipline and those with notoriety. There is at least one person in the discipline or in the field whom they really admire. She has given me the most support, that is unwavering particularly in difficult times. She also taught me love of literature. My mother is a ferocious reader of novels, poetry and history, and is an arts enthusiast. My mother gave me that, which has helped me shape my ability to be able to see problems in an interdisciplinary way.

Varma:

Were you a full time or part time student? Did you have a job in addition to going to school? If you could comment on the economic aspect of being in college.

Payton:

I was a full-time student. Let's see. One year I worked in a retail shop selling jewelry in a mall. Everybody wants to say they worked in the lab; they did this research, and they did all these wonderful things. The reality is that I did those things had engineering and tech internships, but I also worked in retail. I worked in banking to help supplement my academic scholarships. I had a number of internships. I think what really sets a student apart is what are they doing to connect other dots. I learned a lot by interning, I learned a lot about myself when I worked in retail and banking.

Varma:

Who did you live with while growing up? What was your parents’ occupations? Professional, Business, Public Servants, Workers or Others. How would you characterise your family’s socio-economic background: Upper class, Middle class, Lower class, Working class, Other.

Payton:

My dad was a semi-skilled worker, and my mom was a semi-professional. Regarding class status (which is constantly changing), my parents were working class and then perhaps maybe lower middle-class maybe somewhere, somewhere between the two. I learned by watching them when they came home and what conversations took place in the home. You learn a lot as a child and that shaped me.

Varma:

Some gender and race specific questions. In your opinion, what was it like to be a woman of colour while study in university?

Payton:

Isolating,

Varma:

Because you did not see women, you did not see women of color.

Payton:

I did not see any black women. I did not see any Hispanic women. I did not see any Native American women. I will share this story with you. I can remember being a doc student and entering the building one day. And I think we were on the sixth floor, the department was on the sixth floor. I remember there were several people on the elevator, and I push the sixth-floor button to go to my department. One of the professors told me that I had pushed the wrong button. In this elevator there were several other Black people who were getting off on one floor while I was going to a different floor. The professor said (while laughing) that I was going to the wrong floor. There were a lot of perceptions about who participates in what discipline.

Varma:

So my next question you already answered. Do any incidents come to mind that were related to being a woman of colour in the program?

Payton:

I have many stories and many Women of Color do.

Varma:

As a woman of color, did you face any challenge from faculty and peers while studying computing? How about at work?

Payton:

Yes. They were (and are) plenty. When it comes from peers, that is causes more pause. There was a perception that I was there because I was the affirmative action hire/candidate. I actually heard that. So, I think there were those types of challenges. There were challenges with faculty as well, but you have to stay focused. And again, I will go back to my ecosystem that I mentioned to you earlier in the conversation and why it was really important to have a support system around you. And having that support system really made a difference for me along with a very supportive spouse.

Varma:

A related question. Do women of color encounter obstacles that men do not? If yes, can you give some examples?

Payton:

Oh, yes. I published a paper on this - images of women in computing that was in CACM. That piece was with a colleague of mine who resides Finland. I think it outlines it best. Women have to be good, and men can be mediocre and that will be okay. Women have to be good - almost exceptional. That is what we found this exceptionalism that when we are here at this space, we are exceptional, but for women of color, we have to be more than exceptional. And what is that? I am not sure what the trajectory is, or the continuum past exceptional is? That is where the differences lies.

Varma:

In your opinion, how are careers with a computing related degree attractive to women of colour?

Payton:

Women are natural problem solvers. You know this multi-tasking, multi-assignment, multi-modal. How do women become more attractive to computing? I think seeing more of yourself in those that are in the discipline. I think it has to be a concerted effort, providing those images, available to young people at a young age, and then supporting them holistically. This goes beyond teaching the youth to code. I also think that it is important that environments are conducive for women of color. It is not enough to fix/sure-up black women or women of color. Resilence is not enough when you still have to navigate and environments that are often toxic. So environments whether it is academia, industry or government, whatever the sector need to reassess organizational climates.

Varma:

What would be your advice to a woman/minority high school senior thinking about computing?

Payton:

Go for it. I think young people today tend to get a really bad rap. But I think young people today are creative, are capable. I think yes, be prepared for all of the standards, requirements and henceforth that come along, but be prepared for the ride. The computing industry, tech, entrepreneurship, graduate education can offer exciting opportunities for young people. And I would encourage them to just go for it. They can come up with new solutions, new ways to address problems. Understand that if you fail, get back on the journey. And, I think it is through that recognition, that failure is a possibility that there can be new trajectories which they can take. So, I tell them to go for it, but keep an open mind.

Varma:

My last question. Is there anything that you would like to add, any comments you would like to make before we end this interview? Is there a punchline which I did not cover?

Payton:

You have covered it really well.

Varma:

I think hope is that it would be an inspiration for others to pursue the degree. So, what about punchline?

Payton:

Punchline… my goal is to leave the space better than what I found it. And that is what I would hope that particularly young people will know that there are those of us that are really working to try to make the space/experience better. So, when they come along, hopefully, they would not have to deal with as much toxicity and can just get to the business of doing the work that needs to be done. In sum, my punchline is that I try to leave spaces better than what I found them.