David Bader, a chair member at Georgia Institute of Technology's Computational Science and Engineering department, presented on predictive analysis using massive knowledge graphs. He discussed the implementation of graph algorithms in various domains such as social networks, transportation systems, storm evacuations, academic research, science, physics, chemistry, and astrophysics. The Spatio-Temporal Interaction Networks and Graphs (STING) group at Georgia Tech is working on solving real-world problems using graph analytics and surveillance. Bader aims to predict catastrophic events before they occur without experiencing similar tragic events first. He emphasized the importance of double-checking and reality checks when working with data and graphs. The presentation highlighted the potential of predictive graph analytics to anticipate past patterns of behavior and detect new threats, making it a crucial area for solving complex problems in various fields. Bader also discussed the use of Neo4j as a platform for building knowledge graphs and running graph queries to solve real-world problems.