Patient Representation
Develop and test patient and disease representations for clinical models. Jointly learning embedding for different domains and further fine tuning or enhancing embeddings for some domains when limited amounts of data is available present significant AI challenges that will force us to produce methodological innovations. We build a comprehensive library for representation learning methods and develop novel methods to address these presented issues. These representation algorithms will be assessed based on their sensitivity to data characteristics including fairness.
Role I technically lead the task force for patient representation for developing novel fair AI models.
IBM Research. Oct 2020 - Present .
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.