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.

RPI StudySafe

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.

COVID Back-to-School

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.

TWed Lightning Talks (Fall 2020)

Plan to join us WEDS, 09 Dec for a very, VERY special TWed as the Tetherless World Constellation holds another "virtual" version of our end-of-term Graduate Research
"Lightning Talks." TWed Lightning Talks are a great way for the TWC community and friends to learn of the wide range of amazing research happening in the Tetherless World, and "a good time is had by all!"

Lightning talks are VERY short --- approx. 2-3 minute! --- summaries by our students of current research work, with no NO SLIDES and only brief "crib notes."

Privacy Attacks in the context of Machine Learning

This talk will be an introduction to the study of privacy attacks in the context of machine learning, aimed at those unfamiliar with the literature. We will discuss who the stakeholders are, what information may be attacked, how it may be attacked, and why. The “how” will be at a high level, illustrated through some specific examples of privacy attacks.

Tackling Health Inequity using Machine Learning Fairness, AI, and Optimization

Miao will discuss the tools and techniques used to assess, visualize, and improve equity in clinical trials. A set of novel equity metrics for clinical trials is constructed from Machine Learning (ML) Fairness Research to quantify inequities of various subgroups defined over multiple demographic or clinical characteristics, such as Hispanic female subjects who are underweight or no-Hispanic black male subjects aged over 64 and with high fasting glucose level.