Project

Twitter Time Machine (TTM) is an immersive environment for discursive analysis of social media crisis response through multidimensional, interactive analysis.

RPI SafeCampus reveals WiFi access point usage and aggregations of wireless users on the campus network at Rensselaer Polytechnic Institute.

RPI StudySafe reveals the anonymous usage of Wi-Fi access points and aggregations of wireless users on the campus network at Rensselaer Polytechnic Institute

COVID WarRoom has been designed to aid in the development of re-opening strategies as we begin the re-opening process. Presently, COVID WarRoom allows the user to select a location for analysis, and then define the parameters by using one of our four predefined Social Distancing models: Linear Auto-SD, Linear Default-SD, Quadratic Auto-SD, and Quadratic Default-SD.

A Web application designed for RPI students to navigate campus safely in the wake of the COVID-19 crisis. It is built on the R Shiny application framework, written primarily in R, with some UI functionality written in Javascript and HTML. The app informs students on how many people are in a given building on campus and gives recommendations based on past data.

The Rensselaer Data INCITE pipeline for undergraduate data science education consists of an early data analytics course followed by applied data science research experiences on real-world problems. Data INCITE results in data science skills and prompts students to pursue further coursework and careers in data science.

In this exploratory study, we scrutinize a database of over one million tweets collected from March to July 2020 to illustrate public attitudes towards mask usage during the COVID-19 pandemic. We employ natural language processing, clustering and sentiment analysis techniques to organize tweets relating to mask-wearing into high-level themes, then relay narratives for each theme using automatic text summarization. In recent months, a body of literature has highlighted the robustness of trends in online activity as proxies for the sociological impact of COVID-19.

To control spread of COVID, we must implement social distancing measures. Rather than arbitrarily implementing measures against COVID spread, we have built a tool that gives you a quantitative approach to controlling spread. COVID Back-to-School is a tool for generating actionable information on how to reopen schools (elementary, secondary, boarding), universities, workplaces, etc. For different settings of the social distancing "knobs,: you can find out how the infection will spread in your school/university/organization. You can tune the knobs until the spread is a tolerable level for you. The settings for these knobs will then tell you what social distancing protocols you need in place to accomplish that level of tolerable spread.

COVIDMINDER reveals the regional disparities in outcomes, determinants, and mediations of the COVID-19 pandemic. Outcomes are the direct effects of COVID-19. Social and Economic Determinants are pre-existing risk factors that impact COVID-19 outcomes. Mediations are resources and programs used to combat the pandemic. COVIDMINDER analysis and visualizations are by students and staff of The Rensselaer Institute for Data Exploration and Applications at Rensselaer Polytechnic Institute with generous support from the United Health Foundation. COVIDMINDER is an open source project implemented on the R Shiny platform.

The inability to share private health data can severely stifle research and innovation in health informatics. Studies based on unpublished electronic medical record (EMR) data cannot be reproduced, thus future researchers are not able to use them to develop and compare new research. This contributes to the reproduciblity crisis in biomedical research. Making open data available for research can spur innovation and research. The public Medical Information Mart for Intensive Care datasets, MIMIC-II and MIMIC-III, are widely used with over 2000 citations reported in Google Scholar in March 2020. But since MIMIC-II and MIMIC-III focus on Intensive Care Unit patients in Boston hospitals, the resulting research may be biased and have limited generalization. The cost and time required, along with re-identification risk concerns make de-identification only a partial solution to this problem.