Upon arriving at my university's sprawling campus, I hadn't even considered research as something I could pursue. I was just coming off a stressful, borderline torturing experience of applying to colleges as a first-generation student, so I needed a second to catch my breath before I could even think of sketching out how I would spend my time in college. Coming into my first year, I was broadly interested in economics since I spent my precious weekends in high school arguing about foreign policy at local debate tournaments. After reading countless research articles on JSTOR and cutting cards for after-school practice in between classes, I was drawn to the methodology sections of these papers. Using economic models to add nuance to the conversations of development, inequality, and war felt like a black box. I knew I had to dig deeper to determine what drew me in.

First Encounters

My proper introduction to research was through a high school friend who attended a university in Atlanta. We had very similar interests and career goals then, so I had to know what she planned to do for our first summer as college students. She told me she was recently accepted to an undergraduate summer research program. In an instant, I was researching everything I could, trying to understand what all of these new words meant. I had yet to learn that political science used quantitative research methods to answer long-standing questions in the field. Even more surprising was that universities funded students to conduct this interdisciplinary research. I felt my world expand over the next couple of months, reading research papers in the library while I wasn't studying for exams and stalking the websites of programs I found interesting during the summer, hoping any university would open their applications earlier than what was advertised. This newfound enthusiasm drove me to take action, even though the timing differed from what I'd hoped.

To my disappointment, the applications did not magically open during the summer, so I had to wait until early next year to apply to undergraduate research programs. After consulting the spreadsheet I compiled over the past few months, I knew I wanted to spend my summer conducting political science or sociology research since most of my coursework focused on international relations and feminist theory seminars. More specifically, I found myself drawn to the intersection between these two fields during one of those late-night reading sessions where, bleary-eyed but captivated, I couldn't put down anything written by Cynthia Enloe or Jill Steans. As the application deadlines approached, I spent my days hopping between buildings on campus, pouring my soul into these essays, really digging deep, and interrogating why I wanted to get into research in the first place. Each draft was found in a Google Doc, shared with anyone willing to read it, and swiftly edited so I could iterate on my ideas as much as possible. After all was said and done, I submitted my applications, hoping I had gotten into a program.

Rethinking the Timeline

Like many others in early 2020, my carefully laid plans were about to face an unexpected challenge. Not only did I get rejected from every program, but most programs were canceled due to COVID-19. I was at home visiting my family once all these rejections rolled in, and I tried my best to remain confident after having the ideal timeline that I had set up shift drastically in my head. I found myself questioning if this was even worth it. All the positions offered for the summer through my university were taken, and I would have needed to email a professor to get a spot. As I spiraled down this whirlpool of misery, I had to remain calm. The world did stop for a couple of months, meaning I could reassess my plan and act accordingly. If I wanted to make myself a better applicant for research programs, both on- and off-campus, I needed to sharpen my quantitative skills to lean into the interdisciplinary nature of the research I wanted to conduct.

I spent that next semester taking an introductory computer science class, learning the basics of programming and applying it through weekly coding assessments. Honestly, it was hard to mold my brain, which was used to essays, free-response questions, and hour-long in-class conversations, to that of a programmer. The transition made me feel rigid and robotic, like I was losing a sense of the identity I had spent all of college building. However, the more I got used to approaching issues like a computer scientist, the more I saw the appeal of applying that thought process to addressing the conjectures of social science. Instead of seeing programming as a chore, I began to view it as another tool in my analytical toolkit. The systematic nature of coding complemented, rather than competed with, my background in critical thinking and qualitative analysis. Late nights in my dorm, wrestling with syntax errors and debugging code, became less about transforming into someone else and more about expanding who I already was. What initially felt like an identity crisis slowly evolved into an exciting revelation that perhaps the intersection of these seemingly opposed ways of thinking could lead to insights neither field could reach alone. With my newfound technical skills and a renewed sense of purpose, I was ready to take the next step in my research journey.

Finding Purpose

I started going through the research laboratories at my university to see what stood out to me. When beginning this scouting mission, where I would first do research, the influence of the computer science class I finished a couple of weeks ago continued to have a salient impact on me. Rather than just wanting to experiment with quantitative means of inquiry while maintaining a qualitative lean, I was beginning to desire to step into the world of computational experimentation fully. While taking notes on everything I saw that piqued my interest, I stumbled upon a website discussing conducting research by combining computer science, social science, and user-centered design principles. Needless to say, I was hooked and needed to learn more.

I could not email my CV fast enough to the first PhD student I could find. After briefly conversing about my goals, I joined the lab the following semester, and I could feel everything fall into place. I was plunged into this new world of human-computer interaction, where we focused on improving online safety for adolescents through machine learning. Our team worked with actual Instagram data from teens, moving beyond the typical public datasets that often relied on adults posing as teenagers. We implemented different classification models to detect potentially risky messages in online conversations. After working with other undergraduates for a few months, we presented the findings of our first study at a conference at my university. Excited to build on this foundation, I spent another year diving deeper into natural language processing research. This time, we explored text summarization models to help researchers analyze extensive conversation data more efficiently. It was fascinating to discover that these models could identify key sentences containing risk indicators, streamlining the annotation process.

Eager to explore more research directions, I returned to my social sciences roots and worked in the international affairs department a few semesters later. I primarily worked on a forecasting system that estimates the monthly risk of illegal leadership turnover globally using machine learning models. This project opened my eyes to how computational methods could bring new insights to traditionally qualitative queries, transforming abstract human behaviors, like enacting a coup, into patterns we could measure and understand from a different perspective. I received funding from my university to spearhead an independent project, where I used a recent coup in Africa as a case study to showcase how well our model had flagged early warning signs months before the event, consistently identifying the region as high-risk. As I tinkered with this system for months, I collaborated with a PhD student under my advisor, writing some public scholarship on coup politics.

Beyond Campus

Due to these new research experiences, I participated in two off-campus research programs during the latter half of my time at university, narrowing down my interests as a potential PhD student. Under the guidance of Jim Thatcher, my first research experience was at the University of Washington, where I explored the use of the mapper algorithm to identify communities of interest, a process typically limited to qualitative methods. My days consisted of grinding away data analysis by calculating summary statistics, building data pipelines, and learning as much linear algebra as possible to keep up with my peers. It was incredibly fascinating to step into the world of computational topology to attack policy problems from a lens only a few peers could see and investigate the limits of cartography within an interdisciplinary context. Looking back, this intensive dive into computational methods fundamentally changed how I view research. What started as an exploration of topological data analysis evolved into understanding how mathematics could illuminate patterns in social phenomena.

During the following summer, I developed assistive technology prototypes for blind and low vision users at Columbia University supervised by Brian Smith. Like the previous research experience, I was immersed in an entirely new realm of inquiry, this time in social computing, where we developed tools to help people interact more fully with their surroundings. This was my first experience with research-through-design, building MVPs as quickly as possible and watching them break even faster. I spent countless hours in the principal investigator's office, mapping out potential research directions and envisioning a better world where our findings could accelerate the time to get there. My passion for research grew to depths I never imagined possible, but I was beginning to reach my limit with academia and felt drawn to explore other paths. Throughout this time, I started to wonder if there was more out there for me besides research, and I ultimately decided to follow my intuition and explore how my skills could be applied in the real world.

Parallel Pursuits

While immersing myself in research, I gained valuable experience through political technology internships. Working for ActBlue, I further developed my technical skills by streamlining data workflows and analyzing donation patterns' impact on election outcomes. A highlight was leading an audit of our fundraising data tool, where, through user studies and redesigns, we created a more intuitive interface that improved donation analysis and internal data categorization. At Swing Left, I developed predictive models for nationwide donation patterns, using machine learning algorithms to analyze vast donor behavior and demographic information datasets. This work revealed insights about giving patterns across regions and voter segments, helping identify untapped donor networks in crucial swing districts. Working closely with field organizers, I refined these models to incorporate grassroots feedback and local campaign dynamics, ultimately creating a more nuanced understanding of political giving that enhanced our fundraising strategies. These experiences embodied what I love about engineering: transforming complex challenges into elegant, user-friendly solutions.

During my time with New America, I researched how digital public infrastructure shapes societal transformation. My work culminated in co-publishing a comprehensive report analyzing digital transformation efforts across the Mekong region, where I studied how different countries were approaching technological modernization and the implementation of digital systems. This investigation gave me deep insights into how digital public infrastructure functions in real-world contexts, analyzing successes and challenges in different cultural and economic environments. This hands-on exposure to international digital transformation efforts enhanced my understanding of how technological solutions can be effectively adapted to meet diverse societal needs while accounting for local contexts. The experience strengthened my ability to analyze complex technological systems and their broader societal implications, complementing my previous work in political technology and data analysis. With research experience and industry knowledge, I was ready for my next professional step.

Current Endeavors

Once I graduated, I committed to taking a break from academia and decided to work in industry full-time. Coming from an untraditional background, I needed better technical foundations to flourish as a PhD student. Consequently, I participated in a six-month-long intensive bootcamp by Capital One, learning everything I needed to be a successful engineer. As expected, it gave me the skills to be more technically savvy and build more ambitious projects. I joined the company's rotational program upon graduation, where I worked in identity and access management, further developing our authorization portal as part of my first of two rotations. Working on a legacy application of that size was undoubtedly interesting, and navigating repositories with hundreds of files was a learning curve. During my tenure, I mastered agile methodologies and production-ready code while taking on leadership responsibilities. Most notably, I managed the design of a new portal for hosting customized plugins for internal stakeholders. Now, I am working on data quality management, ensuring our information is clean, reliable, and ready to drive business decisions. Despite my professional growth, this journey hasn't been without its challenges.

Hidden Obstacles

While I am eternally grateful for opportunities that have been given to me to do incredible research at institutions I have been dreaming of stepping foot on since I was a child, it did remind me of how high the barrier to entry is for computer science research for those with non-traditional backgrounds like myself. This has led to tons of imposter syndrome, and I constantly ask myself if going down this path was worth it. At the beginning of university, I dreamed of becoming a professor teaching the next generations of political scientists with perspectives on formal theory and quantitative methods. Still, I was determining if that path was right for me after my computer science research exposure.

The disconnect between technological advancement and social science expertise has become increasingly hard to ignore. While shaping our collective future with their products, massive companies consistently sideline social scientists until they are forced to confront the human consequences of their innovations. This pattern also extends into academia and industry, where roles in human-computer interaction paradoxically prioritize computer science degrees despite the field's fundamental focus on human behavior and user experience. Perhaps most frustrating is the hollow promise of interdisciplinary collaboration from institutions and venues that claim to value diverse perspectives while keeping social scientists at arm's length, missing countless opportunities to incorporate novel research findings that could inform technological development. While I understand that our complex systems of being cannot be reduced to features in a machine learning model, it would be helpful to be in the rooms so our human experiences do not get flattened for the sake of algorithmic utility. These obstacles, while daunting, have ultimately strengthened my resolve and clarified my vision for the future.

Towards the Future

I do not want to sound melodramatic, but I know that being a researcher is what I am meant to do with my life. The synthesis of discovery and purpose resonates deeply with who I am, and I want my findings to better the lives of the people around me, whether in the classroom or out in the world. Building tools that help those with similar backgrounds, non-traditional researchers who want to be a part of an ever-quantizing world, fills me with inspiration that I can't begin to describe. I find this inspiration hitting me at work while sitting at my desk waiting for a Jenkins build to run, writing out ideas in my journal, whether disjointed, half-baked notions for conference papers or pages full of in-depth user study scripts. Each day in industry teaches me something valuable that I'll bring back to academia, from software engineering skills to understanding real-world constraints. My experiences outside of research aren't a detour from my path, but they enrich my perspective, which will make me an influential scholar when I return. I will eventually find my way back to research in some form within the next couple of years. I know that for a fact. We will have to wait to see when that next chapter of my life will begin.