Our lab develops an open-source text mining software called NimbleMiner (http://github.com/mtopaz/NimbleMiner). We will work on improving the software using the latest machine learning techniques.
Analyze data from one or more of the following Library Applications/Systems and create visualizations that highlight the most important findings related to our goal of supporting self-directed learning.
Our goal is use a large pool of homecare data (including structured data, free text clinical notes, and recorded patient-provider phone conversations) to build predictive models that help identify patients at risk for poor outcomes (like hospital admission or falls).
CTV’s core mission is to facilitate the transfer of inventions from academic labs to the market for the benefit of society. In a typical year, CTV receives ~400 inventions, completes ~100 licenses and options, and helps form ~20 startups. A good video summary of CTV is here: https://vimeo.com/110193999.
Analyze data from one of the following library applications/systems and create visualizations that highlight the most important findings pertaining to the support of self-directed learning: Vialogues (TC Video Discussion Application), PocketKnowledge (TC Online Archive), DocDel (E-Reserve System), Pressible (Blogging Platform), Library Website and Mobile App.
The DSI Center for Data, Media & Society is seeking undergraduate and masters students during the summer to work on projects at the intersection of Computer Science, Data Science, and the humanities. These projects will combine domain expertise in the humanities with computer and data science techniques to tackle important societal and media problems. Projects can vary from documenting human rights violations, providing rural farmers with financial safety-nets, analyzing the sources of social media popularity, and more!
