Ms. Maham Fatima Fatima
About me
Maham Fatima is a PhD researcher in Artificial Intelligence for Healthcare based in Hamburg, Germany. She works on explainable and ethical AI-driven decision support systems, with a focus on neuro-symbolic reinforcement learning for medical triage in mass casualty incidents. Her PhD is fully funded through the Pro Exzellenzia Scholarship, awarded for academic excellence and leadership potential. She has international academic and research experience through the Erasmus Mundus Joint Master’s program.
Maham Fatima is a PhD researcher in Artificial Intelligence for Healthcare at HAW Hamburg, Germany, specializing in explainable and adaptive decision-making systems for critical medical applications. Her doctoral research focuses on developing neuro-symbolic reinforcement learning frameworks that integrate clinical triage rules with adaptive AI policies to support ethical, transparent, and real-time medical decision-making in mass casualty incidents.
Her PhD research is fully funded through the Pro Exzellenzia Scholarship, a competitive program awarded to outstanding women researchers in recognition of academic excellence, research potential, and leadership capability. This funding supports her independent research trajectory and long-term commitment to advancing responsible AI in healthcare.
Maham holds an Erasmus Mundus Joint Master’s degree in Medical Technology and Healthcare Business, completed across Germany, Portugal, and France. During her studies, she developed strong interdisciplinary expertise spanning AI-driven medical simulations, regulatory and ethical frameworks such as the EU AI Act, and healthcare innovation management. Her academic projects include multi-agent Unity-based simulation environments for emergency response, genetic algorithms for triage optimization, and applied AI for biomedical signal processing.
She also gained industry research experience through her master’s thesis at SONY Europe, where she worked on hyperspectral imaging and deep learning models for remote heart rate estimation, contributing to fair and inclusive health monitoring solutions. Earlier, she worked as a software engineer, developing scalable backend systems and secure APIs, strengthening her ability to translate research into real-world, deployable healthcare technologies.
Her research interests include AI for healthcare, explainable and ethical AI, neuro-symbolic learning, biomedical signal processing, multimodal AI, human-AI interaction, and AI governance. She is particularly motivated by the application of AI to strengthen healthcare systems in low-resource, disaster-prone, and underserved settings.