RESEARCH

Designing for Language Minorities [In Progress]

Team: Max Nguyen; Advised by Ge Gao.

In the United States, there are more than 20% of residents whose native or family language is not English, which is the socially dominant language of the nation. The limited English proficiency population alone, takes up to 9% of the U.S. population. The language barrier poses great challenges in their daily activities, such as health-information seeking, housing and transportation.

Our project aims to investigate the language and technology practice of these language groups, and propose designs to support their language needs in everyday scenarios.

Understanding and Designing for Linguistically Diverse Teams [2020]

Team: Dennis Asamoah Owusu, Emily Gong, Shaan Chopra; Advised by Marine Carpuat and Ge Gao.

With the growth of cross-national companies and international organizations, it is common to see linguistically diverse teams located in different countries solve tasks together. One unique aspect is that non-native English colleagues often switch between their native language and English when meeting with local vs. remote co-workers. This practice benefits the local communication, but also hinders the information exchange at the team level.

Our Project explored the effects of machine translation on the collaboration of linguistically diverse teams, and investigated how to better leverage technology to facilitate team meetings. [Poster Paper] [Full Paper to Appear]

Empirical Study of Participatory Sensing in the Context of Dockless Bikesharing [2019]

Advised by Ge Gao.

Have you ever come into a misplaced shared bike or scooter on the street? These misplaced bikes at dispersed locations of a city could be headache to users, passengers or service providers. Participatory sensing (PS), a sensing paradigm that relies on human using personal mobile device to collect surrounding data, brings promise to the problem. However, we still know little about the practice of PS under the current context, such as the motivation of volunteers.

We conducted a qualitative study to understand people’s concerns, motivation and practice of PS in the ecosystem of bike-sharing programs in China. We identified problems and proposed design implications from there. [Full Paper]

Human-centered Design to Promote Safe Child-Directed Play [2019]

Team: Tab Zhang, Lily Dunk, Leyi Sun; Advised by Lana Yarosh.

Do you miss your childhood where you freely play with your friends in the neighborhoods? Previous research indicates that loss of local activities and mobility may leave children unprepared for transitions to adulthood and make them less independent and may have negative health effects. In this project, we proposed HelloBox, an embodied technology system including an app, wearable RFID tags, and checkin stations for parents providing limited supervision when kids play outside in the neighbors. It aims to improve children’s independent mobility and encourage social interactions between facilities within neighborhoods. [Poster Paper]

Science Communication on Twitter: Investigating Dissemination Pattern [2019]

Team: Leyi Sun; Advised by Estelle Smith

As a prominent online social-networking platform, twitter may provide unique opportunities to enhance public trust in science. In this project, we looked into the media production pipeline for scientific news. In particular, we build ML models and run statistical testing to investigate features that are helpful to identify twitter users (e.g., researchers/journalists), and effects of communication patter (i.e., one vs. two-way communication) on user engagement (e.g., number of likes).

Data Visualization: Geographic Distribution of Bikesharing Resources in St. Paul, MN [2019]

Team: Ge Yu, Songyan Wu; Advised by Daniel Keefe.

In this project, we utilized the public datasets from NiceRideMN and census data, and visualized the geographic distribution of bikesharing resources in the city of St.Paul using Javascript. One of our preliminary findings uncovers the geographic bias of bikesharing distribution, that people who live in lower-income areas may not receive as many as bikesharing resources.