Predictive Modeling of Patient State and Therapy Optimization
This project develops and validates effective predictive modeling technology to achieve the following sepsis treatment related aims on high dimensional and noisy data at a clinically relevant scale
AIM 1 - Personalized sepsis therapy optimization for an individual patient’s state improvement.
AIM 2 - Early diagnosis of sepsis and accurate detection of change in the state of sepsis., and
AIM 3 - Gene expression analysis for sepsis biomarkers identification.
Role I was leading the first and second aims where I developed novel ideas for early detection of sepsis, which resulted in publication in high prestigious conferences such as KDD and SDM.
Temple University. DARPA (DARPA-N66001-11-1-4183). Aug 2011 - Aug 2015 .
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.