In this edition of Cosmographer Corner, we highlight the work of former University of Arkansas at Little Rock graduate and cosmographer Dr. Nihal Hussain. We are extremely proud of Dr. Hussain’s accomplishments! 

Dr. Hussain—who is now a lead data scientist for Equifax—started his graduate education at UA Little Rock in 2014, studying for a master’s in information quality. In his second year, Dr. Hussain decided to pursue a PhD in computer & information sciences. He received his PhD in computer & information sciences in 2019, and worked as a postdoc at COSMOS afterwards. We interviewed Dr. Hussain on where his career is now and what his work at COSMOS entailed, with his responses below.

How did COSMOS fit into your university/secondary education career? How did you come across COSMOS, and what were you studying when you joined COSMOS?

I started out in the master’s information quality program. Before coming to the U.S. and UA Little Rock, I had a software engineering background; I was working in the tech industry. Even before UA Little Rock, I was making mobile apps and was primarily focused on the software side. But then at UA Little Rock I was introduced to the importance of data and the value of it. In data analysis, quality of the data plays a big role. So that’s the reason I had joined the master’s program: to understand the data side of things. In my first semester, I took Prof. Agarwal’s information system analysis course, which is how I met him. I knew he was one of the few professors actively researching, and I wanted to have some kind of hands-on experience. So I joined his lab, with other summer program students and with PhD students guiding me.

I initially started as a data curator, where my primary responsibility was to collect data sets or to crawl data from blogs, especially for the blogtracker tool. I used a web scraper tool to crawl the data from different blog sites that we had identified as potential propaganda websites. As I started crawling, I found some data quality issues, so we worked on that—on how to improve data, and how to create a framework to store data appropriately. Prof. Agarwal helped me understand why we were doing certain things (when it came to data quality) to analyze and pull insights from the data.

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?

I first entered into the whole plethora of blog data analysis that COSMOS was doing. But we moved on from there into YouTube, when we were able to collect data sets from there. We were seeing that the propaganda actors were moving from text-based platforms to video-based platforms because videos are easier to consume and have more visibility. So we started with blog tracking, which grew further into tracking YouTube content, building what would become the VTracker tool.

But in general, we started with studying questions like, “How does fake news spread? How is it formed? What are the different platforms that are being used? What kind of techniques do the perpetrators use to disseminate disinformation?”

I had many colleagues at COSMOS that I collaborated with on different projects. For example, Prof. Samer Al-Khateeb and I worked on YouTube data analysis. With Prof. Agarwal’s supervision, we worked on network and content analysis and combined insights for a holistic view. Over the years, through Prof. Agarwal’s grants, the research started to grow from fundamental data analysis to examining fake news dissemination, information diffusion theory, the Ukraine-Russia crisis, elections, COVID-19-related misinformation, the Asia Pacific conflicts, and other issues. We had a large range of topics that we could work on. At some point, I was also part of the focal structure analysis project, working with Dr. Mustafa Alassad. With Dr. Adewale Obadimu, I worked on inorganic behaviors on YouTube—how to identify them, what are the different patterns that we can build, and how to characterize time series analysis and temporal patterns to discover and model those behaviors.

Since leaving COSMOS, what roles/positions/jobs have you had? What is your current work?

I joined Equifax, a credit bureau, in May 2021 as a lead data scientist. The first responsibility of my role was to support and build analytics that could help identify fraud, to help financial institutions make better decisions. Ideally, we don’t want a fraudster to be able to get access to someone else’s credit card. So the idea is to build models, like time series analysis or entity resolution, to better identify if the incoming request that we are getting belongs to a particular consumer and is not fraudulent—more on the ID fraud side of things, or identity protection.

But then I moved on from there into supporting a broader team. The broader team was working primarily on entity resolution and how we can improve the quality of matchmaking: that is, building a holistic consumer profile that better supports consumers. An example is how most credit bureaus and banks are now starting to not look at just the credit card and financial system information but also rental history or your utilities, to help provision the credit better and make it more accessible for folks who did not previously have access to credit. 

Then earlier this year, just a few months ago, I moved to a bigger role as a data science manager role leading a team. Now I lead a team of six folks that cut across consumer and commercial space to better identify a consumer’s profile better, creating more opportunities for consumers like mom and pop stores (small businesses). Arkansas, for instance, has monoliths like Walmart, but we also have small stores that want to get better credit opportunities, to build a better business and livelihood. So the commercial wing of my team works on that side to see how we can provide better credit for these particular businesses, using the information in their consumer and commercial transactions history. 

What positions did COSMOS and your classes at UALR best prepare you for?

So the classes might not directly translate to the work, but the experience and the learnings from the classes often do. I might be taking a class on big data technologies, like data protection privacy. Now there are laws behind that which we learn about, about what can be done or should not be done—but then applying them from the business standpoint does not always translate one to one. There is a bit of that you have to take from the theory, but then move into the practical side. Because in business, the environment is generally very different and a bit behind the pace with which the academia moves. COSMOS, as an example, is doing video analysis like VTracker or of TikTok, but for industry, it takes a while to adopt that.

But, especially, the good thing about working in COSMOS is you get to work on diverse projects. When I was at COSMOS, I was able to work on network analysis, using network graph software like Gephi, that helped me understand how networks can be used to pull insights; and I also learned text analysis, such as propaganda analysis. I was able to cut across multiple domains and experiences from all of that work that now help a lot on the job because of the diversification of the tools and things I was able to learn.

There was one point that I remember distinctly that Prof. Agarwal had said to me, “Nihal, a PhD may or may not directly prepare you for the next job, but it gives you the stamina and the endurance to be able to think through and solve any difficult problem by breaking it down into smaller chunks, making you more able to critically think and come up with better and holistic solutions.”

If you had to describe the most momentous event at COSMOS, what would it be?

Obviously, getting the PhD was a big thing, but there were multiple smaller events that impacted me. One memorable event was traveling with Prof. Agarwal to Riga, Latvia to train military officers and public affairs officers from various NATO member countries. Prof. Agarwal supported my travel and gave me an incredible opportunity to engage with military information officers to evaluate their needs and refine our research and tools to suit their needs. Prof. Agarwal and I trained over 30 people on the tools COSMOS has developed. It was similar to the training events Prof. Agarwal conducts at the COSMOS research center. We worked with our research collaborators from Carnegie Mellon University and Arizona State University. That collaboration and presentation was quite memorable. 

What advice would you have for current Cosmographers?

That’s a difficult question. The overall culture that COSMOS has is a helpful structure. The team still has the Friday meetings from when I was in the program where one member presents a paper or a chapter from a technical book or journal. Those meetings helped me continue to learn and grow, staying up to speed with how the technology and research landscaping was changing.

Sometimes, it felt intensive having to deliver on other projects and then also give these presentations and reports, but the whole intensive structure that Prof. Agarwal set up helped me a lot in the long run. These things translate to a good extent to the industry. For instance, the technical work that we do may or may not directly translate, but the communication side—writing research papers, building these presentations—translates a lot. Being able to clearly explain what the science means—its significance and implications—for any audience (not just other data scientists), will help anyone be successful in their career.

In the short term, you may not realize this, but in the long term, all of this training and that stamina and endurance that you build, and the analytical thinking culture that we have, will help you in the long run.