My Summer at the University of Chicago Crime Lab and Education Lab

Rhea Mendiratta, MPP Class of 2026, shares about her summer internship with the University of Chicago Crime Lab and Education Labs.

This summer, I worked as a Data Analytics Intern with the University of Chicago Crime Lab and Education Lab, working on Elevate—a personalized learning model implemented by Chicago Public Schools to close the education gap in high-need communities. The project sought to understand whether personalized learning can meaningfully improve academic outcomes for students and, importantly, whether those impacts differ by race, gender, or baseline performance. 

Coming into this internship, I wanted to test my first-year learnings from Harris in the wild—especially my newfound programming skills in R and Python. I wanted to move beyond tidy, classroom datasets and wrestle with the messy, ambiguous, real-world data that actually drive policy research. At the Labs, I got to do exactly that. 

I spent most of my time on the Elevate evaluation running regressions, conducting subgroup analyses, and quality assuring code. Each step helped me practice clean coding, collaboration, and patience. I learned how to use Git and Hopper, structure directories for shared projects, and troubleshoot regressions until they worked and made sense. Working closely with the Elevate team helped me transform from someone who was hesitant to work with coding to someone calm and confident in the coding process. 

Beyond technical growth, the internship gave me a window into how robust, scientifically run research labs operate. I witnessed how teams navigate missing or incomplete administrative data and make decisions under ambiguity—decisions that can quietly shape research validity and, eventually, real policy outcomes. While I had seen on-ground, applied research in my work prior to Harris at a nonprofit that designs large-scale solutions to problems in India’s public education system, I learned a lot about best practices engaging with the other end of the research spectrum. I also appreciated how intentional the Labs are about creating learning spaces. Through workshops, seminars, and coffee chats, I picked up tools and perspectives that extended beyond my Core project, ranging from GIS applications in education research to coding principles, code automation, and randomization inference. 

These experiences gave me a deeper understanding of the big picture of education policy—how we design, evaluate, and scale interventions that work, and what my role in it would be. Watching the Crime Lab’s rigorous and methodical approach reaffirmed the value of grounding big ideas in good data and careful analysis. 

Now, in my second year at Harris, I’m building on this foundation by pursuing the Data Analytics Specialization and the Certificate in Computational Social Science, sharpening my intervention design skills, and continuing to explore questions that sit at the intersection of data and education. 

After my summer internship, I am equipped  with stronger skills and bigger questions: 

• How do we design education systems that are both scalable and rigorous? 

• What role can technical tools—data, GIS, automation—play in sustainable policy design? 

• How can we balance iterative fieldwork with scientific evaluation to drive meaningful change? 

This internship reaffirmed why I came to graduate school—to learn how to build that bridge between data and design; between curiosity and change.