Dr. Butros Maher Dahu, PhD, MBA, MS, is a distinguished researcher, educator, and data scientist specializing in Health and Biomedical Informatics. With more than seven years of academic and professional experience, Dr. Dahu has established himself as a leading voice in applying artificial intelligence (AI), machine learning (ML), and deep learning to improve healthcare delivery, public health policy, and population health outcomes.

Dr. Dahu earned his Doctor of Philosophy (PhD) in Health Informatics from the University of Missouri–Columbia, where his dissertation, “Exploration of Geospatial Artificial Intelligence (AI) Using Remote Sensing to Discover Climate, Environment, and Anthropogenic Features Related to Population Health Diseases,” advanced the integration of AI-driven geospatial analytics into health research. He also holds a Master of Science (MS) in Health Informatics and Bioinformatics from the same institution, a Master of Business Administration (MBA) in Health Sector Management and Finance from DePaul University, and a Bachelor of Science (B.Sc.) in Biology and Chemistry from the University of Houston.

Academic and Research Leadership
During his tenure at the University of Missouri, Dr. Dahu served as a Senior Researcher and Lecturer in the Department of Health Informatics, where he led multiple research initiatives focused on data-driven health solutions. His expertise encompasses deep neural networks, remote sensing, geospatial AI, and the application of big data analytics for understanding complex relationships between environmental and health variables. Dr. Dahu has taught graduate-level courses including Data Mining and Information Retrieval, Advanced Methods in Health Data Science, and Data Science for Healthcare, mentoring numerous students in cutting-edge computational techniques and ethical data practices.

Publications and Scholarly Impact
Dr. Dahu has co-authored more than 18 peer-reviewed publications in prominent journals such as the Journal of Medical Internet Research (JMIR), International Journal of Environmental Research and Public Health (IJERPH), and The Journal of American Medical Informatics Association (JAMIA). His recent studies explore the predictive modelling of obesity prevalence using satellite imagery (JMIR, 2024), the reliability of large language models in healthcare information delivery (MDPI, 2025), and geospatial relationships in COVID-19 testing and socioeconomic factors (MEDINFO, 2023).

His contributions extend to book chapters under review with Taylor & Francis, exploring the intersection of Generative AI and user innovation in public health. His interdisciplinary scholarship integrates computer science, public health, and environmental data, reflecting a holistic approach to solving modern healthcare challenges.

Professional Experience
Before joining academia, Dr. Dahu worked in the healthcare industry, holding positions such as Healthcare Consultant at Generations Healthcare Network and Healthcare Insurance Specialist at Northwestern Medicine. In these roles, he gained practical insights into healthcare operations, budgeting, and insurance systems, which continue to inform his research in health systems optimization and data analytics.

Service and Recognition
Dr. Dahu has served as a journal and conference reviewer for several major publications, including MDPI’s COVID, Atmosphere, and Informatics journals, and he was a recipient of the 2024 AMIA Reviewer Award from the American Medical Informatics Association (AMIA) for his outstanding service to the research community (AMIA Summit Awardees, 2024). He is also an active member of professional organizations such as AMIA and the MUIDSI Graduate Student Organization, where he previously served as Vice President.

Technical and Linguistic Proficiency
A multilingual scholar fluent in Arabic, English, and French, Dr. Dahu is proficient in programming languages and tools essential to modern data science, including Python, R, SQL, PyTorch, and Scikit-Learn. His technical skillset supports his work in machine learning modelling, geospatial analytics, and deep convolutional neural networks (DCNNs) applied to real-world health datasets.