Turning data into impact: Student interns advance public good through summer research

Sara: Over the past weeks, I have been working on the impact of data centers on communities, with a primary focus on the economic aspects. I was very excited to work on that project because I am a double major in computer science and economics, and it was therefore the perfect fit for me.

I applied economic concepts to investigate the impact of data centers, drawing on my computer science skills. During my time at DSPG, one of the activities I enjoyed most was our weekly progress reports, as they were an opportunity to learn more about projects I was not working on. I even learned more about Random Forest regression because one of the cohort members was using it for their project, and after further reading, I realized how much the concepts I have learned in my data structure classes can be applied to solve real-world problems.

After graduation, I plan to attend graduate school, and this experience has genuinely reinforced that decision. It gave me enough exposure to research to realize that it is something I truly enjoy and something I can use to make a positive impact on the world. This experience ended up being so much more than I had hoped for; it exceeded my expectations in every way. I learned many new skills, gained deeper insight into what graduate school and research actually look like in practice, had inspiring discussions with professionals across interdisciplinary fields, and expanded my network in ways I did not anticipate coming in. 

Ziad: This summer I have been working on looking into the impact of data centers on communities for the Virginia Land Use-Value Assessment program, with a focus on the heat effect that each facility releases. I ranked this project as my top choice, as I live in Northern Virginia, where large amounts of these data centers are being built, and it is such a current and important issue.

These facilities are currently receiving tax incentives in the state of Virginia to entice development, but our heat and economic findings together may show policymakers that these incentives might need to be reconsidered. We used difference-in-difference models to compare land surface temperature in areas immediately surrounding the facilities to further control rings.

It was a good experience working on this kind of causal analysis project where I was able to use the VT ARC high-performance computing clusters to handle large satellite data files. I gained valuable experience with difference-in-difference models and spatial analysis, which I hope to continue to use in the future. 

Suyog Gautam: I came into Virginia Tech's Data Science for the Public Good program as a graduate mentor with a geography background, so maps, satellite imagery, and spatial analysis were familiar ground. The economics side was not. Sitting in on conversations about regression discontinuity and difference-in-differences, methods used to figure out whether a policy caused a change rather than just happening around the same time, stretched me in ways I did not expect. The other graduate mentors and our faculty leads were patient with my questions and took the time to walk me through the reasoning, and I learned a great deal from them over the course of the summer. 

I also got the chance to work with a group of really talented interns from a range of academic backgrounds, and they brought plenty of new ideas to the table that I would not have thought of on my own. The collaboration between the economic and geospatial sides of our team proved genuinely meaningful. Overall, these ten weeks were inspiring, productive, and a lot of fun. If I take one lesson forward, it is that the most useful public-interest research happens where methods from different disciplines are forced to talk to each other.

Sophia: This summer, I was part of the Rural Health and Infrastructure team, where we explored how infrastructure accessibility — including transportation, broadband availability, and healthcare access—relates to health outcomes such as chronic disease prevalence and mortality. I was drawn to this project because I had thought that financial barriers were the biggest challenge in healthcare accessibility, and this project offered the opportunity to explore other relationships such as transportation barriers and internet access. 

Our project was conducted in collaboration with Dr. Paula Masters, Vice President of Health Programs at Ballad Health, an organization focused on improving healthcare access in Appalachia. Dr. Masters provided valuable feedback and helped us understand the real-world importance of our work. I also greatly appreciated the guidance of my graduate mentor, Pragati, who helped me navigate more complex methods of analyzing the data. 

One of the most impactful parts of the program was sharing progress with my peers each week and seeing how different teams approached data challenges. These discussions allowed me to learn from other projects and mentors while gaining a broader perspective on the role of data science for the public good. 

Through this experience, I developed skills in data cleaning and spatial analysis while learning how to apply them to meaningful public health questions.  I learned that healthcare accessibility is a multidimensional challenge that requires combining many types of data to fully understand it. DSPG changed my understanding of data science by showing me that it can do more than support business decisions—it can uncover insights that help communities, inform policy, and address complex issues. 

Pragati Dahal

This summer, I had the opportunity to serve as the graduate lead for the Infrastructure and Health Outcomes team, where we partnered with Ballad Health to examine how community infrastructure shapes health outcomes. 

Along with my main project, I also contributed to another DSPG team investigating how the rapid expansion of large data centres influences local temperatures and surrounding economic activity. Applying my background in economic modelling to a new policy question broadened both my technical expertise and research perspective.

One of the most rewarding aspects of the summer was growing as a mentor. As someone who is usually on the receiving end of mentorship, it was incredibly fulfilling to guide undergraduate interns, provide feedback, and watch them navigate real-world research challenges. 

In addition to my mentorship role, I also developed and delivered two hands-on workshops on data visualization and causal inference, creating opportunities for experiential learning and knowledge sharing across the programme.

Above all, I will remember the strong sense of community. After the demanding pace of my first year of PhD coursework, working closely with undergraduate interns and my fellow graduate leads was inspiring. The experience strengthened not only my research skills but also my confidence as a leader, mentor, and collaborator, making this one of the most valuable experiences of my graduate school journey.

It was MY pleasure to work with such impressive students and work products. I look forward to continued collaboration!

Paula Masters, Ph.D.
VP, Chief Health Disparities Officer
Ballad Health

Alicia: When I was selected to be a part of a project that involved art, I thought that I would dread the next ten weeks ahead of me. Now, finally nearing the finish line, I could not have been more wrong. 

Throughout my 10 weeks, I learned more and more about how the arts contribute to the economy. While my previous experience in economic work were soley based on robotics or engineering-based projects, this internship taught me that experiences help expand that thinking beyond the knowledge of what we are content with.  

While I was learning R, along with index construction, I noticed how valuable that knowledge would be in any career I choose to be in. Data analytics is everywhere in modern-day society, and any job in CS will most likely require it. Given this experience, I am more than glad to say that I have learned valuable knowledge I will be taking with me to the workforce. 

Additionally, working with my project partner (David) was nice. Considering our dynamic, we both communicated what we were doing throughout our project to ensure no one was lost in the process of our project development. Alongside this communication, we both contributed to the design of the app and how we could engineer this to be more engaging to a wider audience.  

The most important lessons I’ve learned from this internship are to not judge something based on your initial thought processes. If I hadn’t kept an open mind, I am sure that the initial “dreading scenario” may have gotten the best of me, but now that I am at the end, I can connect the dots and start to understand that this experience was more than meant for me. Furthermore, as I move on with my career, I will most definitely be conducting more economic and artistic-based projects/research further into my career.  

David: On my first day of the Data Science for the Public Good program, I was assigned to work on a project where I was to create an “arts vibrancy index.” My initial thoughts were ones of much confusion. How could data be connected to art? 

The main goal of this project was to compare three different methods of constructing an index and see which method would make for the most interpretable and communicable index. Learning more about index construction as a general statistical methodology was my favorite part of working on this project, but learning more about the value of art in society also added to the experience.  

I did not just learn more about statistics and data analytics, though. I realized more about just how important the aspect of communication truly is. Perhaps I can understand my own work and my own results, but can others also understand? I realized that I could answer just about any question I had regarding my work, but were my answers clear enough to the ones who were asking the questions? Throughout this project, I was particularly able to learn more about how to appeal to a variety of audiences. With some audiences, it may be appropriate to get all technical, but other audiences may just need a general idea. 

I now come out of these ten weeks with not just more knowledge in the statistics field, but also more knowledge about the value of art and its impact on society. I also leave having a much better sense of how to communicate across a wide range of audiences. 

Angana Chatterjee

This summer I served as a graduate mentor on the Art Vibrancy in Virginia project, alongside co-mentor Xiaofan Zhu and a team of undergraduate researchers, in partnership with the SMU DataArts Research Team and the Kohl Centre. Our goal was to build a Virginia-specific, county-level version of the national Arts Vibrancy Index, a composite measure of nonprofit arts organizations, arts-related employment, and government grant funding. This is designed to make rural counties visible in a framework where they currently aren't. I took on this project because it let me apply my training in economics to a question with real stakes for arts policy in Virginia. 

A large part of my work was methodological. The national AVI uses factor analysis to weight its thirteen component variables, but factor analysis behaves unpredictably with the kind of data Virginia produces: many rural counties report zeros across entire categories, and the method can amplify sparse non-zero signals in ways that distort rankings. I, along with Xiaofan, guided the team through building two parallel indices: one equally weighted, one factor analytic.  The framing of the comparison is not a contest between methods but as a robustness check. The key finding was that roughly 95% of counties shifted by five ranks or fewer between the two approaches, with disagreements concentrated in exactly the sparse rural counties where zero-inflation creates instability. That result told us something important: the index is measuring something real, not an artifact of how we built it. 

Our final analysis confirmed a widening gap in arts vibrancy between Virginia's metro and rural areas, driven largely by how grant funding is distributed. What I'll carry forward is a sharper sense of how to position empirical work for non-technical audiences without sacrificing precision. The AVI project reminded me that a ranking is only as useful as the story someone can tell with it, and that story must be honest about what the data can and cannot support. 

It was a pleasure working with David, Alicia, and everyone in the team. We greatly appreciate the thoughtful development and innovative approaches the team brought to calculating and interpreting the AVI, and assessing the robustness of different methodologies.

We were especially impressed by how the team worked with the complex data, addressed its limitations, sought out local partners, and delivered an effective presentation and poster within a short period of time. The AVI is one of our key products, and your work has provided us with a revised framework and several promising ideas for further testing and expansion. You have contributed not only to the continued development of the AVI but also to the broader arts and culture sector by improving how arts vibrancy is measured and interpreted. 

We'd like to sincerely thank all the fellow students, faculty, and staff who supported this collaboration. We are also grateful for the USDA grant that made the program possible.

We look forward to continuing the conversations as we explore scaling this work to the national level.

Wenhua Di
Research Director, SMU DataArts, Meadows School of the Arts
Research Professor, Arts Management and Arts Entrepreneurship & Economics
Southern Methodist University

Kyle: This summer, I worked on a research project assessing the effectiveness of California Assembly Bill 413, a policy aimed at reducing pedestrian-related traffic injuries and fatalities. I was excited to work on this project because, through collaboration with our stakeholder, California Walks, a local advocacy group dedicated to creating safe and walkable streets for all Californians, our work has the potential to influence future lifesaving policymaking.  

Working alongside another intern, Pride, our objectives were first to assess how the law has been implemented throughout jurisdictions across the state and, second, to create a model that captures the law’s impact. While we worked on this project, we had a large network of graduate and faculty mentors to support and guide us. The most impactful part of the experience was the ability for our team to execute a research project from start to finish, and to do so while communicating with an external partner who is interested in using our work to create real change.  

The challenge of safe roads has been ever-increasing in California, with a steady rise in pedestrian-related accidents. While the state has made attempts to combat this rise, there is minimal existing knowledge on the true success of these policies. We hope that looking at the impacts of previous policies, such as this one, is the first step toward more effective future policymaking and saving lives.  

While I learned several technical skills during this internship, such as econometric methods like Regression Discontinuity in Time, more importantly, I learned soft skills of how to work in a research team and communicate findings with an external partner and general audience. An experience that I will remember from this project is conducting a spatial analysis of the crash data- something that I think is very interesting to visualize. This project solidified my understanding that data science can be used as a tool to create positive change.   

Pride: Joining the Daylighting Project gave me a new perspective on how data science can improve people's lives. Our team examined California's Assembly Bill 413, a law designed to improve pedestrian safety by increasing visibility at intersections. Working with California Walks, our community stakeholder, made the project especially meaningful because our findings had the potential to inform future transportation policy and public safety decisions. 

One of the most impactful parts of the experience was learning to evaluate whether a public policy was truly making a difference. Although I had no prior experience with causal inference methods, I gradually learned how to use them to separate the effects of the law from other factors that influence crashes. More importantly, I learned that meaningful research stems from asking the right questions and understanding the assumptions behind every analysis. This taught me that good data science is not about proving a hypothesis but about letting the data tell an honest story. 

Apart from technical depth and analysis, this program helped nurture other important skills. Conducting literature reviews helped me understand existing research and identify important knowledge gaps, while frequent presentations enhanced my confidence and made communicating complex findings to both technical and non-technical audiences much more seamless. Above all, this experience showed me that curiosity, collaboration, and clear communication are just as essential as coding when using data science techniques to address real-world challenges. 

Yuanyuan Wen

This summer, I had the opportunity to work as a graduate mentor with our fabulous interns, Pride and Kyle, on a project assessing the effects of California’s traffic safety policy, AB 413 (also known as “Daylighting”).

Watching them proactively solve problems and approach the project with genuine curiosity was incredibly inspiring and reminded me to bring the same spirit to my own research. We explored many different ways to examine whether the effects of AB 413 varied across jurisdictions. Although we did not find the exact patterns we had expected based on our theory, the process itself was a valuable reminder that research rarely unfolds exactly as planned and that unexpected findings can often lead to new questions and insights.

Beyond gaining mentoring experience, which I know will be important for my future career in academia, this experience also gave me a different perspective on my own research struggles. Seeing the research process from the mentor’s side helped me reflect on my own work, particularly how to stay curious, persistent, and open-minded when the results do not immediately align with our expectations.

I am grateful to Pride and Kyle for making this such a rewarding experience. I learned a lot from mentoring them, and I hope they also took away something valuable from our time working together.

"I was really thrilled with the work that the students did, which reinforces the outcomes we already see in other parts of the world. I look forward to future opportunities to collaborate and partner with the program." Kevin Shin, Co-Executive Director, California Walks