Our project, sponsored by Included AI and Info Edge, focuses on enhancing data reliability through an AI-powered data quality checker. The solution identifies anomalies, standardizes inconsistent entries, and visualizes data integrity metrics, ensuring clean and trustworthy datasets for business insights. We developed automated pipelines that detect and correct real-world data issues, such as inconsistent location naming and missing fields. An intuitive dashboard allows stakeholders to monitor data quality over time. By integrating AI with usability, our tool reduces manual cleaning effort, increases trust in analytics, and empowers data-driven decision-making across enterprise teams.

We leveraged the nascent AI native RAG and Graph Database to develop a resume screening and shortlisting tool. Traditional ATS systems often present challenges for hiring managers and recruiters. Strict text-matching methods may screen out capable, qualified candidates who word their resumes differently from the job description. To address this issue, we employed a GraphDB to uncover latent relationships among entities, enabling a more semantic understanding of candidate profiles. Additionally, we packaged these capabilities into a chatbot interface, allowing hiring managers to interact with resumes and job descriptions using natural language.

Low income populations often lack access to health insurance that enables them to test for illnesses. Tuberculosis alone carries an estimated price tag of $287 per test in the United States. In an effort to counter this out-of-pocket expense, we constructed an advanced machine learning model that predicts the likelihood an individual has Tuberculosis with high accuracy. The prediction is based on symptoms, demographics, and a self recorded cough recording, and the process is quick and easy with the help of a chatbot. By using our application, individuals can make an informed decision about whether medical intervention is necessary.

Working for the Book Club of Washington, a bibliophile society dedicated to book collecting and book arts, I created an online public access catalog for the organization's independently published periodical, the Journal of the Book Club of Washington. To do so, I used LibraryThing to create bibliographic records for over 300 of the Journal's articles from its debut in 2000 to Spring 2023. I then organized those records into an OPAC utilizing LibraryThing's TinyCat software. Additionally, I digitized each issue that lacked a digital copy and made the digital versions available to the public via the online catalog.

The Ladies Musical Club (LMC) is Seattle’s oldest arts organization. Founded in 1891, LMC helped establish Seattle as a musical city by giving skilled women musicians a platform for public performance, thrilling audiences with concerts by world-famous musicians, and supporting other Seattle institutions like Cornish College. LMC’s important history is well preserved but can be hard to find. This project aims to improve accessibility to the LMC’s archive by providing a cross-institutional collection guide and enhancing the LMC’s digital archive. These changes offer a clearer window to Seattle’s music history for LMC members, researchers, music fans, and the broader public.

In July of 2021 the Reader Services department at The Huntington Library expanded their access policy, resulting in a more diverse population of patrons. This project aimed to determine what services could be improved or employed to better serve Huntington researchers through an accessibility assessment of the special collections reading room and the online process users must undergo to obtain a reader card. The assessments revealed a need to create intentional space for readers with disabilities, both in person and online. Recommendations included reading room alterations, the implementation of an accommodations request libguide, and updated language throughout the library's website.

Vanport Mosaic is a community archive that preserves memories related to life in Vanport, Oregon, the flood that destroyed the city, and the reverberating legacies of displacement, discrimination, resistance, and resilience that followed. This project addresses Vanport Mosaic’s need to make its content available online by developing a rights and usage policy, designing a metadata schema, and populating descriptive metadata for the collection’s oral history interviews to increase discoverability. Through this work, the marginalized histories in this collection will become discoverable and accessible to the public, making them available to educate, inspire, and advocate for change.

Our project addresses the challenge of distinguishing authentic news from misleading content in today's rapidly evolving digital landscape. Utilizing advanced machine learning models—SVM, Logistic Regression, Naive Bayes, and XGBoost—we compared their efficacy in accurately classifying news articles into four categories: Human Real, Human Fake, Machine Real, and Machine Fake. Our results highlight SVM's accuracy and stability, making it the most effective model for this task. By enhancing the detection and prevention of misinformation, our project significantly contributes to public access to credible news, ensuring that individuals can make informed decisions in a world increasingly influenced by AI-generated content. This effort not only enhances public information integrity, but also addresses the essential need for instruments that can prevent the sophisticated spread of false news.