Prospective Analysis of Large and Complex Partially Observed Temporal Social Networks

Aug 1, 2012 · 1 min read
projects

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 .

Temple AI Research AIML
Mohamed Ghalwash
Authors
Mohamed Ghalwash
Associate Professor
Mohamed Ghalwash is an Associate Professor of Artificial Intelligence at Ain Shams University / Zewail City of Science, Technology, and Innovation. Ghalwash is interested in developing machine learning and AI models for discovery of interpretable spatio-temporal patterns from complex events and exploiting their utilities in practical high impact real-world applications, focusing on analytics for healthcare, health behavior, decision support, and to support the generation of real world evidence from medical data, wearable devices and instrumented environments. He also has more than ten years of expertise in the software engineering industry, developing software that are currently being deployed, where he engaged in all phases of the project life cycle for the development of large business applications. My long-term goal is to put my footprints in the field of pattern recognition through contribution of original ideas and translating those ideas to end applications and products.