Alexander Mantzaris is an Associate Professor for the Statistics & Data Science Department. Research interests for the past 5-6 years have focused on Social Physics which is the field seeking to explain sociological phenomena using principles of physics. This research direction has focused primarily on statistical mechanics to examine the state trajectories in simulations. More recent work in this area maps the sociological processes to fully defined thermodynamic processes which provides a different view of the phase changes. The aspiration of this direction of research is to help identify criticality points in complex social phenomena. Another key vein of research has been in Graph Convolutional Neural Networks and how they can be used in social network analysis but this direction is being replaced with LLLMs (foundation models) as they are proving capable of representing the association information effectively. Prior work has involved visualization of Big Data sets efficiently and with minimal cognitive load, and the interface between Big Data and ML for real time processes.