Voice to Vision: Enhancing Civic Decision-Making through Co-Designed Data Infrastructure

As trust in community engagement and democratic decision-making declines, there's an urgent need for increased transparency and accountability in civic processes. For this project, I collaborated with the New York City Department of City Planning to co-design data infrastructure that enhances civic decision-making. Using a research-through-design approach with both planners and community members, we developed an interoperable data structure and an interactive visualization system with two key components: a community-facing platform that clearly demonstrates how public input informed decisions, and a planner-facing tool that supports the complex sensemaking process of analyzing community feedback. Voice to Vision addresses critical gaps in current engagement processes by providing communities with clear responses to their input while offering planners essential support for analyzing diverse feedback. This project contributes to the participatory design space while offering practical tools that can help rebuild trust in civic decision-making processes.

This is an ongoing project!

Evaluating Narrative Strategies for Constructive Community Engagement

In contentious community issues like school redistricting, effective communication is essential but challenging. For this project, I'm collaborating with Winston-Salem/Forsyth County Schools to investigate how narrative strategies can foster understanding across diverse perspectives during their redistricting process. I developed and evaluated 124 AI-generated, human-reviewed narrative summaries that distill community input from over 13,000 comments, 8,300 survey responses, and 170+ hours of audio recordings. Through a field deployment, user studies with community members, and controlled experiments, I'm examining how varying the balance between concrete experiences and abstract opinions in narratives influences readers' understanding and engagement. This research explores how AI can help create narratives that reflect both diverse and shared experiences within large communities, and how narrative strategies can effectively demonstrate that community input was meaningfully considered in decision-making processes.

This is an ongoing project!

Coalesce: An Accessible Mixed-Initiative System for Designing Community-Centric Questionnaires

Effective community engagement is crucial for inclusive governance, but civic leaders often struggle to design questions that gather meaningful input due to time constraints and limited experience. For this project, I developed Coalesce, a mixed-initiative system that leverages AI to help civic leaders craft tailored and impactful questions for surveys, interviews, and conversation guides. Drawing on questionnaire design best practices, Coalesce improves question readability, enhances specificity, and reduces bias. The system was developed through interviews with 30 civic leaders and 14 iterative feedback sessions. In real-world evaluations with 16 participants using Coalesce for their own community projects, we found it improved their confidence in questionnaire design, supported diverse workflows, and fostered learning while raising important considerations about human agency and AI reliance. This work demonstrates how intelligent user interfaces can help civic leaders engage more effectively with their communities.

BoundarEase: Fostering Constructive Community Engagement to Inform More Equitable Student Assignment Policies

School district attendance boundaries significantly impact educational access and equity, but community engagement processes for changing these boundaries are often polarizing and ineffective. I collaborated with a large US school district serving nearly 150,000 students to design BoundarEase, a web platform that helps community members explore and provide feedback on potential boundary changes. Through formative interviews with 16 community members, we identified key challenges in existing engagement processes: individualistic thinking, lack of empathy for different perspectives, and difficulty understanding policy impacts. The BoundarEase platform addresses these frictions by visualizing proposals and facilitating structured feedback based on community preferences. Our user study with 12 participants showed that BoundarEase encouraged people to consider impacts beyond their own families and increased transparency around policy proposals. This project offers both a practical tool for school districts and insights into how technology can reduce polarization in local educational policymaking.

Sage: Design Implications of a Task-Oriented Hybrid Chatbot for Strategic Storytelling

While storytelling is essential for self-advocacy, people often struggle to develop and deliver strategic narratives, especially in high-stakes situations. For this project, I'm developing Sage, a hybrid architecture chatbot that supports strategic storytelling by combining traditional rule-based systems with the generative capabilities of large language models. Through evaluation with 21 experts across negotiation, communications, and HCI fields, we've demonstrated the effectiveness of this hybrid approach while exploring the fundamental tension between authenticity and impact in AI-assisted storytelling. Our findings reveal important design considerations for conversational AI systems, particularly around memory management and preserving narrative authority. This work offers insights into how AI can empower marginalized voices while maintaining the structural effectiveness required for high-stakes communication.

This is an ongoing project!

SenseMate: An Accessible and Beginner-Friendly Human-AI Platform for Qualitative Data Analysis

Many community organizations want to engage in conversations with their constituents but lack the support they need to analyze feedback through qualitative data analysis (QDA), or sensemaking. As a result, it's important to provide accessible entry points into the analysis process for people with no prior experience. For my master's thesis, I designed SenseMate, an AI-based platform to support non-researchers in qualitative coding. After developing a codebook, or a list of themes, from a subset of the data, qualitative coding involves applying the codebook to all the data. SenseMate aims to transparently recommend themes for pieces of text to increase the efficiency and reliability of qualitative coding. Through an online experiment with 180 novice users, we found that SenseMate increased intercoder reliability by 29% and improved coding accuracy by 10%. The platform features rationale extraction models that provide explainable AI recommendations while preserving user privacy and control, making it uniquely suited for community organizations with limited resources.

Future CMS Schools

I collaborated with an amazing team of people from CCC and Charlotte-Mecklenburg Schools (CMS) to create a new form of community engagement around two magnet schools opening in the fall of 2023. Through facilitated small-group conversations and various online platforms that we designed, we were able to more deeply engage CMS’ parents, students, and other community members in dialogue around their hopes and concerns for the new schools.

Facilimate

I applied user-centered design methods to create a digital support tool for people who facilitate small-group conversations. Using Facilimate, facilitators at any skill level can more easily manage time and follow conversation guides.

Shared Mobility Visual Analytics Tool

I created a visual analytics tool that can help city planners manage their shared mobility services. The tool is a React web application that analyzes scooter-share event data, estimates spatial demand, and generates an interactive data visualization page where users can map out usage and demand.

How to Not Get Rich: An Empirical Study of Donations in Open Source

Open source is ubiquitous and forms the digital infrastructure of our society, yet sustaining open source has become increasingly difficult due to growing demands and developer burnout. Donations are gaining in popularity as a potential method of sustaining open source. This research project is the first large-scale study to investigate the prevalence and impact of donations on open source.

The Relationship between Public Transit and Bikeshare Ridership

In this data science project, I explored the causal relationship between bikeshare and public transit networks in Boston, Philadelphia, and Washington DC using doubly robust estimators. I applied the economic concept of complements and substitutes to analyze how bikesharing could be used to support or supplant the first-mile/last-mile problem.

TASBE Flow Analytics

TASBE is a user-friendly and open-source environment that visually represents and analyzes flow cytometry data. In addition to feature development, I worked closely with biologists to design a customizable Excel interface that enables biologists without programming skills to set up analysis workflows in TASBE.

Interactive Robotics Research

I collaborated with three other students to analyze robot vision data using python OpenCV to program a robotic arm to autonomously play the card game SET (project 1) and to replicate user-built cube structures (project 2).

Computational Microbiology Research

I worked on several computational microbiology research projects with the goal of generating unified theories of microbial community function that impact all aspects of our lives, ranging from the environment to human health. I applied my programming and data science knowledge to analyze metagenomics data to understand nutrient cycling, apply network analysis to examine the effects of perturbation on microbial communities, and write software that mines genome databases.