Structured Regression in Complex Networks by Fusion of Qualitative Knowledge and Big Data
Integrating context, domain knowledge, and data-driven modeling of complex phenomena based on observations that are highly structured and interdependent is a very challenging task. In this project we address this problem by using the structured predictive modeling framework based on a probabilistic exponential graphical model instead of relying on traditional statistical approaches that assume independent and identically distributed random variables. Current state-of-the-art predictive modeling techniques usually cannot deal with such large and complex networks; thus we provide knowledge-based compression techniques for complexity reduction. We use multiple kinds of domain knowledge and context to capture additional information that might be missing from the observed data. We also directly constrain the model optimization based on domain constraints and embed other problem-specific qualitative knowledge directly into the framework. The main innovation of this project is extending our structured learning models to explore in detail the hypothesis that a unified approach of integrating big data with sources of high-level knowledge (ontologies, domain-based constraints etc.) is beneficial for predictive modeling of complex phenomena.
Role I have developed predictive models for BIG temporal graphs, which resulted in publications in IEEE Big Data conference.
Temple University. ONR (N00014-15-1-2729). Jun 2015 - May 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.