Published on April 8, 2025
Updated on April 14, 2025
Dr. Hatef Dastour joined the MU Institute for Data Science and Informatics as an Assistant Teaching Professor of Data Science and Analytics in April 2025. He specializes in geomatics, remote sensing, data fusion, geospatial analytics, big data analytics, and mathematical and statistical modeling. His research integrates advanced computational methods to address critical challenges in environmental processes, climate change, water resources, and natural hazard management.
He holds dual Ph.D. degrees in Applied Mathematics and Geomatics Engineering, with a focus on computational methods and geospatial analysis for environmental sustainability, climate change adaptation, and water resource management. His interdisciplinary research applies machine learning, big data analytics, and agent-based modeling to environmental challenges. He has conducted research at the University of Calgary, with funding support from the Government of Canada and the Government of Alberta.
Before joining MU, he held academic positions at Mount Royal University, the University of Lethbridge, and the University of Calgary as an instructor and postdoctoral associate. His research explores the application of machine learning, environmental modeling, and data-driven decision-making in natural disaster management, with a focus on forest fires, floods, droughts, and other climate-induced hazards. He develops predictive models for land-use change detection, hydrological systems, and climate impact assessments to enhance resilience and inform sustainable water resource management strategies.
He also brings extensive industry experience as a Data Scientist and Research Scientist, leveraging data-driven solutions to address real-world challenges such as business optimization, environmental analysis and modeling, and decision-making. In these roles, he developed data-driven solutions using Python and SQL, designed scalable data pipelines, optimized predictive models, and integrated diverse data sources for large-scale analytics and decision-making. His expertise extends to big data processing, machine learning workflow development, and database engineering.
He actively contributes to the academic community through graduate thesis examination, journal reviewing, conference organization, and academic leadership roles. He has served as an external examiner for graduate theses, reviewed articles for leading journals in remote sensing, environmental modeling, applied mathematics, and data science, and played a key role in organizing conferences and seminar series. He has also held elected leadership positions, including serving as a graduate student representative on academic committees and founding the SIAM Chapter at the University of Calgary to foster collaboration between academia and industry. Additionally, he has engaged in science outreach and volunteer activities, including mentoring students, organizing workshops, and participating in community-based initiatives.
In addition to his research, he is a dedicated educator. He has taught courses in Machine Learning Systems and Digital Engineering, covering advanced machine learning techniques, data-driven decision-making, computational modeling, automation, and optimization in engineering workflows.
Beyond academia, he is a technology enthusiast who enjoys hiking and values work-life balance, believing that meaningful engagement with family and friends enhances his effectiveness as an educator and mentor.