Dr Masoud Mahdianpari, Memorial University of Newfoundland/C-CORE, Canada
Based on the provided information, Dr. Masoud Mahdianpari is indeed a strong candidate for the Research for Research Hypothesis Excellence Award. His extensive educational background, professional experience, and contributions to the field of remote sensing and data science highlight his qualifications.
Publication profile
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Educational Background
Dr. Masoud Mahdianpari holds a Ph.D. in Electrical Engineering from Memorial University of Newfoundland (2015-2019), along with a Master’s in Remote Sensing Engineering and a Bachelor’s in Geomatics Engineering, both from the University of Tehran (2010-2013, 2006-2010). His robust academic foundation has equipped him with advanced knowledge in remote sensing and data analysis.
Professional Experience
Currently serving as a Cross-appointed Professor at Memorial University of Newfoundland and Remote Sensing Technical Lead at C-CORE, Ottawa, Dr. Mahdianpari has significant experience in applying machine learning and remote sensing technologies. His previous roles include Remote Sensing Scientist and Research Assistant at C-CORE, where he has developed expertise in image processing, feature extraction, and target detection.
Research Expertise
Dr. Mahdianpari specializes in machine learning, big data technologies, and radar remote sensing. His work encompasses high-resolution image processing, environmental monitoring, and GHG emission estimation. He is leading several projects focused on wetland mapping and methane emission estimation in the Arctic, leveraging advanced remote sensing data and cloud computing platforms.
Professional Appointments
As an associate editor for various journals, including IEEE Geoscience and Remote Sensing Letters and Frontiers in Environmental Science, Dr. Mahdianpari contributes to the academic community and promotes high-quality research. He is a member of several professional societies, such as IEEE and ASPRS, demonstrating his active engagement in the field.
Recent Honors and Awards
Dr. Mahdianpari has been recognized for his contributions to science, including being ranked in the top 1% of scientists worldwide (2023-2024) and receiving multiple awards for his research excellence. Notably, he has secured grants such as the NSERC Discovery Grant (2022-2027) and the Microsoft AI for Earth grant, highlighting his innovative work in environmental monitoring.
Project Leadership
Dr. Mahdianpari is currently leading the ESA Carbon Science Cluster project, aiming to enhance methane emission estimates from wetlands in the Arctic. This project underscores his leadership in addressing critical environmental challenges and advancing remote sensing methodologies.
Research Interests
His research focuses on environmental monitoring and wetland mapping using remote sensing data, emphasizing machine learning and multi-sensor image classification. Currently, he leads projects related to greenhouse gas (GHG) monitoring, showcasing his commitment to addressing pressing environmental issues.
Project Experience
He currently leads a project for the European Space Agency focused on improving methane emission estimates from wetlands, an initiative of significant environmental importance. This role emphasizes his leadership in research that impacts global environmental policies.
Publications and Presentations
Dr. Mahdianpari has authored numerous influential publications, including studies on remote sensing image classification and advanced machine learning applications in environmental monitoring. His research has contributed significantly to the field, evidenced by his citations and presentations at major international conferences.
Conference Contributions
He has presented at several prestigious conferences, showcasing his research on water quality monitoring and electrical potential preservation. His publications in leading journals further establish his reputation as a thought leader in remote sensing and environmental science.
Conclusion
In summary, Dr. Masoud Mahdianpari’s outstanding qualifications, research contributions, and recognition in the field make him a highly suitable candidate for the Research for Research Hypothesis Excellence Award. His dedication to advancing remote sensing technology and addressing pressing environmental issues through innovative research exemplifies excellence in academic and applied research.
Publication top notes
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review
Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review
Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery
Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery
The first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the google earth engine cloud computing platform
Deep convolutional neural network for complex wetland classification using optical remote sensing imagery
A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem
Comparing deep learning and shallow learning for large-scale wetland classification in Alberta, Canada
A systematic review of landsat data for change detection applications: 50 years of monitoring the earth
Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: a comparative evaluation