SenseMate: An AI-based Platform to Support Qualitative Coding

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’m designing 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.

This is an ongoing project!

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.