Large-scale particle data sets, such as those computed in molecular dynamics (MD) simulations, are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles and their local density can be perceived well in close-up views. However, for large-scale simulations with 10 million particles or more, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry. Excessive super-sampling can alleviate this problem but is prohibitively expensive. In this dissertation, we present novel visualization methods for large-scale particle data that address aliasing while enabling interactive high-quality rendering by sampling only the visible particles of a data set from a given view.
He earned his Bachelor’s degree in Computer Engineering from the American University in Cairo (AUC) in 2010. Later on, he obtained a Master’s degree in Computer Science from KAUST in 2012.
Mohamed Ibrahim's research is concerned with Large-Scale data visualization. He's especially interested in visualizing data obtained via Molecular Dynamics (MD) simulations. In addition, he did some research in 3D shape deformation, 3D shape synthesis, and 3D shape editing.
Mohamed Ibrahim earned many awards during his bachelor studying. He received the Exemplary Student Award and the Certificate of Academic Honor from the CSE Department at the American University in Cairo in 2010. In the same year, he was awarded the Corporate Life Competition (CLC) Winner from the Olympic Group in Egypt.