This paper introduces an interactive visualization interface prototype tailored for electrical power grid system planners and analysts to facilitate the exploration of historical outage data and gain actionable insights. With the advent of smart sensors, data generation has exponentially increased. As the amount of data has significantly grown, so has the need to effectively monitor it. Our prototype addresses this challenge by seamlessly integrating heterogeneous datasets such as GIS, AMI, and OMS. Our interface is designed to empower system planners and analysts in assessing the performance of distribution systems, focusing on circuit efficiency and outage events. This paper describes the functionalities of the visualization interface and presents its example usage scenarios.

This paper introduces a novel visual analytics approach, DCPViz, to enable climate scientists to explore massive climate data interactively without requiring the upfront movement of massive data. Thus, climate scientists are afforded more effective approaches to support the identification of potential trends and patterns in climate projections and their subsequent impacts. We designed the DCPViz pipeline to fetch and extract NEX-DCP30 data with minimal data transfer from their public sources. We implemented DCPViz to demonstrate its scalability and scientific value and to evaluate its utility under three use cases based on different models and through domain expert feedback.

This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioeconomic factors on COVID-19 clinical severity by employing multiple Recurrent Graph Neural Networks. We designed and implemented a visualization interface that leverages coordinated multi-views to support exploratory and predictive analysis of hospitalizations and other socio-geographic variables at multiple dimensions, simultaneously. By harnessing the strength of geometric deep learning, we build a consensus machine learning model to include knowledge from county-level records and investigate the complex interrelationships between global infectious disease, environment, and social justice. Additionally, we make use of unique NASA satellite-based observations which are not broadly used in the context of climate justice applications. Our current interactive interface focus on three US states (California, Pennsylvania, and Texas) to demonstrate its scientific value and presented three case studies to make qualitative evaluations.