Mohammad Marjani | Environmental Science | Best Researcher Award

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Mohammad Marjani | Environmental Science | Best Researcher Award

Memorial University of Newfoundland | Canada

Dr. Mohammad Marjani is a dedicated researcher and academic specializing in remote sensing, geospatial intelligence, and artificial intelligence applications for environmental monitoring. He is currently pursuing a Ph.D. in Electrical and Computer Engineering at Memorial University of Newfoundland, where his research focuses on developing advanced remote sensing and deep learning algorithms for environmental and climate-related analysis under the supervision of Dr. Masoud Mahdianpari. He earned his Master of Science in Geospatial Information Systems from K.N. Toosi University of Technology, where he conducted innovative research on wildfire spread modeling using deep learning techniques. His undergraduate degree in Geodesy and Geomatic Engineering from the same university explored 3D change detection methods in point clouds. His academic journey reflects a strong interdisciplinary foundation in remote sensing, GIS, machine learning, and computer vision, particularly applied to natural disaster management and environmental systems. Dr. Marjani has contributed as a peer reviewer for high-impact journals such as IEEE Geoscience and Remote Sensing Letters, Theoretical and Applied Climatology, and Remote Sensing. Professionally, he serves as a Research Scientist at C-CORE, where he develops AI-driven environmental modeling algorithms using satellite data. Alongside his research, he has demonstrated academic leadership through multiple teaching assistantships, delivering courses in C++, MATLAB, and Python programming across topics such as computational intelligence and image processing. He is also a co-founder of GeoHoosh, an educational group dedicated to promoting artificial intelligence applications in geomatics and geospatial engineering. Dr. Marjani’s research interests encompass wildfire modeling, satellite image analysis, WebGIS, and GeoAI, reflecting his commitment to advancing the integration of artificial intelligence with geospatial sciences for sustainable environmental solutions.

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Featured Publications

  • Marjani, M., Ahmadi, S. A., & Mahdianpari, M. (2023). FirePred: A hybrid multi-temporal convolutional neural network model for wildfire spread prediction. Ecological Informatics, 78, 102282.

  • Marjani, M., Mahdianpari, M., & Mohammadimanesh, F. (2024). CNN-BiLSTM: A Novel Deep Learning Model for Near­-Real-Time Daily Wildfire Spread Prediction. Remote Sensing, 16(8), 1467.

  • Marjani, M., & Mesgari, M. S. (2023). The large-scale wildfire spread prediction using a multi-kernel convolutional neural network. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 10(4/W1-2022), pp. 483-488.

  • Marjani, M., Mohammadimanesh, F., Varon, D. J., Radman, A., & Mahdianpari, M. (2024). PRISMethaNet: A novel deep learning model for landfill methane detection using PRISMA satellite data. ISPRS Journal of Photogrammetry and Remote Sensing, 218, 802-818.

  • Bahrami, H., McNairn, H., Mahdianpari, M., & Homayouni, S. (2022). A meta-analysis of remote sensing technologies and methodologies for crop characterization. Remote Sensing, 14(22), 5633.

Mohammad Ali Nasiri | Materials Science | Excellence in Citation Achievement Award

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Mohammad Ali Nasiri | Materials Science | Excellence in Citation Achievement Award

university of valencia -Instituto de Ciencia Molecular (ICMOL) | Spain

Dr. Mohammad Ali Nasiri is a distinguished researcher and innovator with expertise in micro- and nano-electronic device fabrication, cleanroom processing, and advanced materials characterization. With over five years of hands-on experience in cleanroom environments, he has mastered key fabrication techniques including thin-film deposition, electrical contact formation, dry etching, and photolithographic mask preparation, all of which are critical to the development of high-performance electronic and optoelectronic systems. His technical proficiency extends to a wide array of advanced characterization methods such as AFM, XRD, XPS, SEM, FESEM, FTIR, spectroscopic ellipsometry, and electrochemical analysis, enabling him to conduct precise evaluations of material properties and performance. Holding dual master’s degrees in Aeronautical Engineering and Nanomaterials Science, Dr. Nasiri integrates interdisciplinary expertise in thermodynamics, applied mathematics, and materials physics to address complex challenges in energy and electronic device research. He completed his PhD at the University of Valencia’s Institute of Molecular Science (ICMOL), where his research, titled “Advances in Hybrid Energy Devices: Integrating Thermoelectric Materials via Fabrication, Characterization, and Modeling,” focused on sustainable energy conversion through thermoelectric materials. His studies on metallic thin films, conductive polymer nanocomposites, and lignin-based membranes yielded significant advancements in thermoelectric efficiency and ionic transport understanding. Notably, he developed three innovative thermal conductivity measurement setups, demonstrating both engineering ingenuity and scientific depth. Currently a postdoctoral researcher at the Institute of Materials Science (ICMUV), University of Valencia, Dr. Nasiri is developing perovskite-based photodetectors for sensor and biomedical imaging applications. His work embodies the fusion of nanotechnology, materials innovation, and sustainability, contributing to the next generation of clean energy and optoelectronic technologies.

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Featured Publications

1. Nasiri, M. A., Cho, C., Culebras, M., & Cantarero, A. (2024). Back mirror-free selective light absorbers for thermoelectric applications. Advanced Optical Materials, 16(12), 2402079. https://doi.org/10.1002/adom.202402079

2. Nasiri, M. A., Cho, C., Culebras, M., & Cantarero, A. (2024). Ultrathin transparent nickel electrodes for thermoelectric applications. Advanced Materials Interfaces, 11(5), 2300705. https://doi.org/10.1002/admi.202300705

3. Muddasar, M., Nasiri, M. A., Cantarero, A., Gómez, C., Culebras, M., & Collins, M. N. (2024). Lignin-derived ionic conducting membranes for low-grade thermal energy harvesting. Advanced Functional Materials, 34(12), 2306427. https://doi.org/10.1002/adfm.202306427

4. Muddasar, M., Menéndez, N., Quero, Á., Nasiri, M. A., Cantarero, A., Gómez, C. M., Culebras, M., & Collins, M. N. (2024). Highly-efficient sustainable ionic thermoelectric materials using lignin-derived hydrogels. Advanced Composites and Hybrid Materials, 7(2), 47.