In this edition of Cosmographer Corner, we highlight the work of former University of Arkansas at Little Rock graduate and cosmographer Dr. Kim Tran. We are extremely proud of Dr. Tran’s accomplishments!
Dr. Tran—who is now a senior manager at Murphy USA—started her graduate education at UA Little Rock in 2005, studying first for her MBA in Business Administration and going on to pursue a PhD in Computer and Information Science, which she received in 2018. We interviewed Dr. Tran on where her career is now and what her work at COSMOS entailed, with her responses below.
How did COSMOS fit into your university career? How did you come across COSMOS and what were you studying when you joined COSMOS?
As you go through your PhD training, you have somebody that is your advisor and mentor. The way that you and your advisor work together is very strategic; in my case, there were initiatives that I knew Dr. Agarwal was involved in. There is going to be some type of research topic that you’re going to focus on. So he and I spent quite a bit of time trying to find a topic that I felt was germane to an area and that I thought I could be impactful in the field. With any PhD, the sky’s the limit—which also means that finding scope can be tough. So we really spent time walking through the potential impact of what was being done—initially just figuring out what the landscape, if you will, was, and then ultimately narrowing down a topic that we thought could be meaningful and provide contribution to the field. In my particular case, I was interested in what had potential business implications. At the time that I joined COSMOS, the space in social networking was beginning to be a little bit more congested, and there was a lot of interest in that area. I was trying to find something novel that would contribute to the field.
In other words, my work with COSMOS and Dr. Agarwal started when I was trying to figure out who I wanted to work with and my research area of focus for my PhD. One aspect was trying to find an area that interested me. And then the other was to find a good kind of dynamic. I visited with several different faculty members to learn about what their interests were, to get a sense of overlap of interest. So that’s really how we started working—I don’t know if ‘organic’ is the right word, but almost: it was based on a series of discussions that we had. And, of course, I had had some courses with Dr. Agarwal, as well. Through that, we were able to learn about things that were of shared interest to him and to me from a research perspective, some of the collaborations that he was engaged in, and I was able to see the potential synergies of working with him and as a team.
How did COSMOS contribute to your career and program at UALR? What was Dr. Agarwal’s role in your journeys during and after?
Currently, I am now in the private sector. The focus is less about research, per se, but there’s research in keeping up with the latest advances. With my work with Dr. Agarwal, a lot of our time was spent honing the topic, the direction and, in some instances, the approach, such as instances where I thought the methodology might need some level of refinement. With regards to building out my dissertation committee, these are all different things that we worked through together. Because at the end of the day, what you want when you’re presenting to the dissertation committee is that there are no surprises. So really, just having him work alongside me as my guide and mentor, that was very instrumental. There was also a point where there were some really amazing opportunities that were provided by Dr. Agarwal when I was graduating. I had actually spent some time talking through the various options with Dr. Agarwal; he was very instrumental in that regard.
How would you describe the “research pipeline” that you worked on while at COSMOS? In other words, what was the specific area in which you researched?
It was mostly about the development of a methodology. As you know, in the social network space, as well as even just social media space, there was at the time a lot of unstructured data. The question I had was, is there a way that you could leverage any of that data—not just at large—but in a way that you could work with a more focused subset of data? Namely, could you leverage it to see if it could provide efficacy in, say, early stage screening for pharmaceuticals, like the phase one of pharmaceutical trials? Take, for example, that you have a population rate of data for, in this case, patients who had idiopathic pulmonary fibrosis—a very localized group who has a unique subset of symptoms as well as a disease. If you could take that, what was the quality of data needed for that particular subset? Was there any industrial application for that kind of data, if you were to apply it to a more industrial setting? So part of my research was building out that methodology and seeing if there were actually a potential use case for that data.
Since leaving COSMOS, what positions have you had? What is your current work? What positions did COSMOS and your classes at UALR best prepare you for?
I have worked in a variety of sectors—everything from big box retail, small box retail, and over to business development and even higher ed. There was one opportunity that just happened to open up, where I had had some really good conversations with the company, around the time I graduated. In this case, the company was Murphy USA, where I now work. I handle pricing for merchandise at the company. It’s a little different than pure research, but some of the applications and use cases are still very germane, including model development, efficacy, data quality, and things of that nature, which all play a significant role in many of the things that we do. What’s fascinating is, of all the different things that I’ve learned over my time in a variety of industries, all of those processes have been highly applicable in what I do in the current day, whether it’s working with teams or working with more technical aspects. For example, if we’re leveraging a model, we need to know: is that model actually good? Does that model have any efficacy? What are the parameters that we need to better tune that model? Those are all things that have actually continued to play a role in how I do business.
If you had to describe the most momentous event at COSMOS, what would it be?
I remember attending a conference that had been founded by some folks over at MIT, that had a curriculum and program design to fit within the industry. I was a co-chair at the time, and another was Bill Inman, who’s colloquially known as the father of data warehousing. He ended up being a keynote speaker at that particular conference. What I remember vividly was a simple message; his takeaway was this: when we created technology as a field, the whole purpose was to solve problems. If we get to a point where the technology doesn’t solve a problem or is too complex, we need to rethink how we’re doing business. I thought that was so simple but also quite profound. It’s so easy to think about the tech stack that you’re using or the number of people that you need to implement a particular technology. But if you can do something that is highly complex in a much more simple manner, then that is worth evaluating. Whether it comes down to algorithm design or leveraging some of the latest features that are available through AI—whatever the case is—at all points one should think: here is this technology I’m using, but is it the optimal way to do this? You can do something super complex, but you should always ask, practically, what is the actual application? Does it have some potential use? And if it’s inefficient, is it actually worth your time?
What advice would you have for current Cosmographers?
For those in the program, I would say first and foremost, learn the latest platforms and technologies that are available. I say this because everything’s kind of converged. For the first time in the history of machine learning—namely AI and algorithms—we actually have AI at a point where it is highly usable. Whether you’re researching or it’s a part of your research agenda or not, you need to learn it. You need to learn how to leverage it because it will be important for the future, due to the implications of the technology. That’s one. Then, two—as I alluded to earlier—but if you have the option, try to find something that is applicable and has a potential business or use case. Applicability is very important.
Then, I would say my last advice is that—whether you’re researching, working part time, and or doing other things—I would encourage everybody to connect, build bridges, and put yourself out there. If you are in any engineering or tech space, it’s all too easy to just be thinking inside your head. You’ve got these ideas, or you’re constantly running math in your mind all the time, and it can be a very introspective process. But if you think of researchers as people, what do people do? People are there to connect with other people so that we’re part of a larger community. So it’s important to really get out there, and that’ll benefit you on a couple of fronts. If you’re a researcher, being able to connect with researchers in another state or multiple states, your research interests may converge. Or if you’re planning to possibly one day enter the private sector, you still never know when an opportunity may arise. Some of the best job opportunities I had happened because I was in the right place at the right time.