Associate Professor of Artificial Intelligence
Email: mghalwash@zewailcity.edu.eg
Office Room Number: Academic Building, Room S017-B3
Program / Department: DSAI / CSAI
Prof. Mohamed Ghalwash
Biography
Mohamed Ghalwash holds a PhD in Machine Learning from Temple University (USA) and earned his BSc and MSc from Ain Shams University. With over 15 years of experience in AI and Machine Learning across both industry and academia, his research focuses on developing interpretable AI, discovering spatio-temporal patterns in complex data, and translating these insights into high-impact applications.
His work bridges foundational machine learning—with publications in NeurIPS, AAAI, KDD, and ICDM—and real-world challenges in healthcare, appearing in prestigious journals such as The Lancet, Diabetes Care, and Scientific Reports.
His contributions include novel frameworks for integrating domain knowledge into deep learning (CHIL), interpretable feature selection (BMC Bioinformatics), and advances in dynamic treatment effect prediction (ML4H) and disease progression modeling (ICHI).
Currently, Dr. Ghalwash leads and supervises a dynamic team of MSc and PhD students engaged in cutting-edge AI research, including cryptanalysis for information security, EEG analysis for brain–computer interfaces, federated learning, explainability, and quantum computing. His role involves not only imparting knowledge but also fostering an environment of innovation and research excellence.
Research Areas
- Artificial Intelligence,
- Machine Learning,
- Pattern Recognition,
- Explainability,
- Fairness,
- Bioinformatics, Health Informatics
Funded Projects
Prof. Ghalwash currently serves as Co-PI on an STDF-funded project for developing AI models for novel vaccine design for foot-and-mouth disease.
Awards & Honors
Prof. Ghalwash's excellence in research has been recognized through publication awards from Ain Shams University (Egypt), multiple Patent Plateau Awards from IBM (USA), and Best Graduate Paper awards from Temple University (USA). Prof. Ghalwash is an active contributor to the global research community where he serves on the Editorial Boards of the International Journal of Data Mining and Bioinformatics (2015–Present), Journal of Biomedical Engineering and Informatics (2016–2019), and Big Data (2020–2024). He is a frequent peer reviewer for premier venues including NeurIPS, ICML, ICLR, IJCAI, AAAI, KDD, SDM, and IEEE TNNLS.