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.


MortalityMinder (MM) is a web-based visualization tool that enables interactive exploration of social, economic and geographic factors associated with premature mortality among mid-life adults ages 25-64 across the United States. Using authoritative data from the CDC and other sources, MM is a freely available, publicly-accessible, open source, and easily maintained tool. The goal of MortalityMinder (MM) is to enable healthcare researchers, providers, payers, and policy makers to gain actionable insights into how, where, and why midlife mortality rates are rising in the United States (US). It is designed to help healthcare payers, providers and policymakers at the national, state, county and community levels identify and address unmet healthcare needs, healthcare costs, and healthcare utilization.

James Hendler

James Hendler
Director, the Rensselaer IDEA & Tetherless World Chair of Computer, Web and Cognitive Sciences

James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI.  He also heads the RPI-IBM Center for Health Empowerment by Analytics, Learning and Semantics (HEALS) and serves as a Chair of the Board of the UK’s charitable Web Science Trust. 

One of the originators of the “Semantic Web,” Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the AAAI, BCS, the IEEE, the AAAS and the ACM. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. He is also the first computer scientist to serve on the Board of Reviewing editors for Science. 

In 2010, Hendler was named one of the 20 most innovative professors in America by Playboy magazine and was selected as an “Internet Web Expert” by the US government. In 2013, he was appointed as the Open Data Advisor to New York State and in 2015 appointed a member of the US Homeland Security Science and Technology Advisory Committee. In 2016, became a member of the National Academies Board on Research Data and Information and in 2018 became chair of the ACM’s US technology policy committee and was elected a Fellow of the National Academy of Public Administration.



Ph.D., Computer Science, Artificial Intelligence, Brown University

Sc.M., Computer Science, Artificial Intelligence, Brown University

M.S., Cognitive Psychology, Human Factors Engineering, Southern Methodist University

B.S., Computer Science, Artificial Intelligence, Yale University


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.