The RHAPSSODY project proposes an automated reasoning platform to extract, understand, and reason with complex arguments. The project is based on combining computational argumentation theory with natural language processing. This approach will identify the most essential arguments listed on debate platforms, estimate the acceptability degrees of these arguments using information mined from the web, and using the totality of the arguments, estimate the decision that will be taken.

A digital twin (DT) is a model of a real system, which automatically learns and continually updates to represent the dynamics of the system in near real-time, using sensor data that reflect various aspects of its operating conditions, human experts with relevant domain knowledge, and its environment. However, this recent concept does not yet benefit from support tools for its effective implementation.

Our computational science capabilities have created a recent explosion of data that is rapidly changing how the scientific community investigates complex phenomenon. In particular, we are now leveraging high performance computing resources to simulate across a variety of applications of interest.The challenges of analyzing scientific data are particularly severe because they are large and unwieldy.

Some grand challenges facing humanity include climate change and health. Unfortunately, implementing effective solutions for these challenges is counteracted by misinformation, disinformation, and malinformation (MDM), e.g., climate change denying or anti-vaccine propaganda. To address this issue, we propose robust and holistic fact verification methods. The proposed methods can reduce data bias, aggregate information across multiple statements, and yield global conclusions.