Assoc. Prof. Dr. Abolghasem Sadeghi-Niaraki, Sejong University, South Korea
Assoc. Prof. Dr. Abolghasem Sadeghi-Niaraki is a distinguished academic in the Department of Computer Science and Engineering at Sejong University, South Korea. Recognized among the top 2% of scientists globally, he specializes in Geo-AI, Extended Reality (XR), and spatiotemporal analytics. With over 15 years of experience, he has led groundbreaking research in AI-integrated GIS systems, XR technologies, and smart city innovations, securing over $9.3 million in funding. He is a Fellow at Harvard’s Spatial Data Lab and a recipient of the Australian Endeavour Fellowship. His work focuses on advancing human-computer interaction, environmental analytics, and immersive technologies for real-world applications.
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Summary:
Dr. Abolghasem Sadeghi-Niaraki is a highly accomplished researcher whose innovative contributions to Geo-AI, XR, and analytics place him as a strong contender for the Research for Best Researcher Award. His pioneering work is backed by an exceptional track record in research leadership, international collaborations, and societal impact.
🎓 Education
Dr. Abolghasem Sadeghi-Niaraki has a robust academic foundation, including a Ph.D. in Geo-Informatics Engineering from INHA University, South Korea, with a dissertation on ontology-based geospatial modeling for personalized route finding. He also holds an M.Sc. in GIS Engineering and a B.Sc. in Geomatics-Civil Engineering, both from K.N. Toosi University of Technology. His postdoctoral fellowships include spatial data infrastructure research at the University of Melbourne and advanced geo-informatics at INHA University.
đź’ĽExperience
With over 15 years in academia, Dr. Sadeghi-Niaraki is an Associate Professor at Sejong University’s Department of Computer Science and Engineering. Previously, he served as an Assistant Professor at INHA University. He has led pioneering projects in XR, Geo-AI, and spatial technologies, securing $9.3 million in research funding and collaborating with institutions and industries worldwide. His leadership roles include contributions to establishing XR research centers and advanced mobile virtual reality initiatives.
🔬Research Focus
His research emphasizes Geo-AI and its application in environmental analysis, XR technologies, and the Metaverse. He has significantly advanced knowledge in human-computer interaction, spatiotemporal analytics, and the integration of AI with GIS systems. His projects address challenges in smart city technologies, disaster management, and cultural heritage preservation using innovative computational models.
🏆Awards
Recognized among the top 2% scientists globally in 2024, Dr. Sadeghi-Niaraki has received prestigious accolades such as the Australian Endeavour Fellowship and the Nationally Distinguished Researcher Award of Iran. His innovative contributions have also earned honors for cutting-edge strategies in flood susceptibility mapping and numerous commendations for his academic and industrial collaborations.
Dr. Sadeghi-Niaraki’s expertise spans Geo-AI, XR, and machine learning technologies. Proficient in tools like TensorFlow, PyTorch, and Apache Spark, he excels in geospatial data analysis, NLP, and the integration of AI in XR platforms. His technical acumen includes extended reality programming, big data analytics, and IoT systems, complemented by a strong foundation in academic writing, grant management, and international research collaborations.
Publications
- Publication: A Review on Mixed Reality: Current Trends, Challenges and Prospects
Citations: 367
Year: 2020
Authors: Rokhsaritalemi, Sadeghi-Niaraki, Choi
- Publication: Ontology Based Personalized Route Planning System Using a Multi-Criteria Decision Making Approach
Citations: 311
Year: 2009
Authors: Sadeghi-Niaraki, Kim
- Publication: Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network
Citations: 271
Year: 2018
Authors: Minh, Sadeghi-Niaraki, Huy, Min, Moon
- Publication: Crop Pest Recognition in Natural Scenes Using Convolutional Neural Networks
Citations: 220
Year: 2020
Authors: Dang, Li, Moon, Sadeghi-Niaraki
- Publication: A Methodological Framework for Assessment of Ubiquitous Cities Using ANP and DEMATEL Methods
Citations: 103
Year: 2018
Authors: Rad, Sadeghi-Niaraki, Abbasi, Choi
- Publication: Real World Representation of a Road Network for Route Planning in GIS
Citations: 91
Year: 2011
Authors: Sadeghi-Niaraki, Varshosaz, Kim, Jung
- Publication: Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review
Citations: 87
Year: 2022
Authors: Zafari, Bazargani, Sadeghi-Niaraki, Choi
- Publication: Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models
Citations: 85
Year: 2019
Authors: Razavi-Termeh, Sadeghi-Niaraki, Choi
- Publication: Ubiquitous GIS-Based Forest Fire Susceptibility Mapping Using Artificial Intelligence Methods
Citations: 67
Year: 2020
Authors: Razavi-Termeh, Sadeghi-Niaraki, Choi
- Publication: A GIS-Based Decision Support System for Facilitating Participatory Urban Renewal Process
Citations: 61
Year: 2019
Authors: Omidipoor, Jelokhani-Niaraki, Moeinmehr, Sadeghi-Niaraki
- Publication: Comparison Between Multi-Criteria Decision-Making Methods and Evaluating the Quality of Life at Different Spatial Levels
Citations: 60
Year: 2021
Authors: Vakilipour, Sadeghi-Niaraki, Ghodousi, Choi
- Publication: Internet of Thing (IoT) Review of Review: Bibliometric Overview Since Its Foundation
Citations: 59
Year: 2023
Authors: Sadeghi-Niaraki
- Publication: Short-Term Traffic Flow Prediction Using the Modified Elman Recurrent Neural Network Optimized Through a Genetic Algorithm
Citations: 59
Year: 2020
Authors: Sadeghi-Niaraki, Mirshafiei, Shakeri, Choi
- Publication: Ubiquitous Sensor Network Simulation and Emulation Environments: A Survey
Citations: 56
Year: 2017
Authors: Sharif, Sadeghi-Niaraki
- Publication: Land Subsidence Susceptibility Mapping Using Persistent Scatterer SAR Interferometry Technique and Optimized Hybrid Machine Learning Algorithms
Citations: 55
Year: 2021
Authors: Ranjgar, Razavi-Termeh, Foroughnia, Sadeghi-Niaraki
Conclusion:
Dr. Sadeghi-Niaraki’s achievements, global recognition, and forward-thinking research agenda make him exceptionally suitable for this award. His work demonstrates a blend of technical depth, real-world impact, and a vision for the future of technology. With a focus on expanding interdisciplinary collaborations and societal outreach, he has the potential to further enhance his already significant contributions to science and humanity.