Congratulations to Ayat Mohammed on Successful PhD Defense
This research examines humans' predictable bias in interpreting visual-spatial, spatiotemporal information, and inference-making in scientific visualization from a visual analytic point of view. I examined different case studies from different domains such as land suitability in agriculture, spectrum sensing in software-defined radio networks, raster images in remote sensing, pattern recognition in 3D points cloud, airflow distribution in aerodynamics, galaxy catalogs in astrophysics and protein-membrane interaction in molecular dynamics. Each case required different computing power, ranging from personal computer to high-performance cluster. Based on this experience across application domains, I propose an empirical paradigm for scientific visualization that supports three key features of scientific data analysis: representations, fusion, and visual discrimination. The results of these studies contribute to our knowledge of efficient MVMD designs and provides scientific visualization developers with a framework to mitigate the trade-offs of scalable visualization design such as the data mappings, computing power, and output modality. Ayat is starting a post-doc at Texas Advanced Computing Center (TACC) in their Visualization group.