Where do you want to immigrate? Immigrants need as much information as possible when adapting to a new country to combat cultural differences and transition smoothly. Without appropriate research, they can face barriers in accessing basic needs and are often subject to exploitation. Immigration Inc. is an interactive website providing knowledge and resources for those interested in moving out of their home countries. Through data visualizations powered by government statistics covering topics such as education, employment, and population, Immigration Inc. enables users to make informed decisions regarding immigration.

For those whose primary language isn’t English, finding books in one’s own culture can be difficult. In our project, we want to connect Indigenous people who speak minoritized languages with public library books in their native tongue. We envision an application built in collaboration with regional public libraries to encode less common languages or translated books in a way easily discoverable. Our target audience is indigenous people seeking to discover more books in their native language. However, we also consider public library systems as another stakeholder in our application, because they will facilitate the upload of data into our application.

We developed a ZEV Dashboard for Kenworth using Tableau to help the decision-making process of the Kenworth Project Planning team. In this way, they will gain more insights into the conversion to ZEVs in response to the rise of CO2 emissions. In addition, to better adapt to future ZEV credit requirements set by the government, Kenworth can take advantage of the ZEV credit calculator and set up plans for future sales of ZEVs regarding climate change.

This project aims to provide assistance to the Health Systems - Maternal Mortality Rates and Drivers (HS-MMRD) team at the Institute for Health Metrics and Evaluation (IHME) on the creation of a health facility classification and healthcare personnel mapping, for use on investigating why maternal mortality ratios have not decreased, despite an increase of deliveries in health facilities. A public interest article was written for the Think Global Health (TGH) website, which illustrates barriers to accessing maternal healthcare in Kenya, and includes an infographic of differences in travel times to birthing facilities for patients in urban versus rural areas.

Masterworks is a full-service marketing and fundraising agency that helps faith-based non-profits achieve their missions through growth strategies focused on quality of engagement with their audiences. Masterworks clients don’t fully understand the effectiveness of digital marketing, which affects the company’s ability to retain clients in the future. Our MasterView Tool enables Masterworks’ clients to see the value of investing in digital marketing. It achieves this goal by showing them the specific ways that a person’s engagement with ads leads to financial donations. With this, Masterworks can validate that their clients' investments are delivering quantifiable results.

Homelessness is a multifaceted crisis that includes many common information and community challenges, such as social instability, systemic inequity, and discrimination. We combine social network analysis, Natural Language Processing, and stochastic topic modeling to measure online information sharing among U.S. homeless care organizations. Analyzing four years of social media data from major federal homeless care communities reveals evidence of a robust-growing homeless service community interacting with the marginalized Hispanic/Latinx population shortly after the announcement of COVID-19. This study offers valuable practical policy implications for U.S. homeless services organizational learning.

In recent years there has been an abundance of mis/disinformation around US elections on social media sites like Twitter. Research collectives such as the Election Integrity Partnership investigated this information in real time to analyze and disseminate important details across election stakeholders. However, not all researchers have the quantitative skill sets necessary to access this data at scale. Our project improves the research process of qualitative researchers by creating a Python Jupyter Notebook that allows researchers to gain insight from datasets relevant to the US election.

Joe, a partner at a VC shares a password with a team of 35 but paid for one seat with PitchBook. This is a violation of the terms of use, poses a security risk, and adds friction to account managers day to day. We implemented an intuitive interface built on a machine learning foundation by finding significant attributes in the user login data that will detect a password-sharing event. The valuable insights on usage data revealed strong correlations between a suspicious session with various types of usage that would help account managers monitor accounts with unusual usage on the platform.

Gender minorities entering the technology field experience imposter phenomenon (IP) at a disproportionate rate as compared to their male counterparts. Phenomenality aims to mitigate the effects of perceived inadequacy by encouraging users to reflect on and celebrate their daily accomplishments in a journal-like manner. Through consistent, affirmative self-talk, a Likert scale quiz, and personalized IP alleviation strategies, Phenomenality provides gender minorities with the resources to optimize their potential, boost their confidence, and ultimately flourish in their professional environments.

To establish trust between the public and the Seattle Police Department (SPD), it is imperative that there is accountability and transparency with SPD’s processes and procedures. In this project, we build a machine learning model that can be used to predict and audit adequate Use of Force (UOF) by officers based on the nature of a dispatch event. The model developed through this project can serve as a tool for the SPD to identify and understand deviance from regular trends to guide police training and will also help to identify whether UOF is being accurately reported.