Prospective Analysis of Large and Complex Partially Observed Temporal Social Networks
The analysis of social networks often assumes a time invariant scenario, while in practice actor attributes and links in such networks evolve over time and are inextricably dependent on each other. In addition, the temporal graph is just partially observed, multiple kinds of links exist among actors, various actors have different temporal dynamics and environmental influence can be both positive and negative. This project is closely examining the hypothesis that a unified approach of jointly modeling these and related problems is beneficial for prospective analysis of large-scale partially observed temporal hypergraphs. Novel methods for analyzing large and evolving graphs developed on the project are evaluated on high impact applications related to predictive modeling of information networks, climate and human health.
Role I have mentored students and participated in developing predictive models for temporal graphs, which resulted in publications in top-tier AI conferences such as AAAI.
Temple University. DARPA (AFOSR award number FA 9550-12-1-0406). Aug 2012 - Jul 2016 .
Mohamed Ghalwash is an Associate Professor of Artificial Intelligence at Ain Shams University and Zewail City of Science, Technology, and Innovation. His research focuses on interpretable machine learning for spatio-temporal pattern discovery, with applications in healthcare analytics, health behavior modeling, and decision support, drawing on prior research leadership at IBM and Temple University. He has supervised eight postgraduate students across Ain Shams University and Zewail City, with thesis topics spanning security applications, Bayesian structure learning, and explainable AI, and currently serves as co-PI on a three-year STDF-funded grant (2026–2029, 2,700,000 LE) developing AI-driven immunoinformatics approaches for Foot-and-Mouth Disease vaccine design.
Alongside his academic research, he brings more than ten years of software engineering leadership across senior roles at IBM, Proteinea, Raya Software, and Asset Technology Group, spanning technical team leadership, software architecture, and large-scale enterprise system delivery. This engineering background has also shaped his applied research, extending into machine learning for security and biotech domains, including cryptanalysis and AI-driven vaccine design.