Muhammad Nouman Noor | Artificial Intelligence | Research Excellence Award

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Muhammad Nouman Noor | Artificial Intelligence | Research Excellence Award

National University of Computer and Emerging Sciences Islamabad |  Pakistan

Dr. Muhammad Nouman Noor is an accomplished researcher in computer vision, deep learning, and digital image processing, with a focus on AI-driven healthcare and software engineering solutions. He has authored numerous high-impact journal articles, conference papers, and book chapters, covering areas such as gastrointestinal disease recognition, skin lesion analysis, and optimization of deep learning models for medical diagnostics. He has supervised multiple master’s theses, bridging theoretical research with practical AI applications, and actively contributes to the scientific community as a reviewer and editorial member for reputed journals and conferences. His work effectively connects industry and academia, advancing machine learning and computer vision technologies in real-world scenarios. He has received over 220 citations, with an h-index of 8 and an i10-index of 7, reflecting the impact and recognition of his research contributions.

Citation Metrics (Google Scholar)

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h-index
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i10-index
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Featured Publications

Jamshaid Ul Rahman | Artificial Intelligence | Editorial Board Member

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Dr. Jamshaid Ul Rahman | Artificial Intelligence
| Editorial Board Member

Abdus Salam School of Mathematical Sciences, GC University Lahore | Pakistan

Dr. Jamshaid Ul Rahman’s research spans artificial intelligence, deep learning, mathematical modeling, and scientific computing, with a strong emphasis on applying advanced neural architectures to complex problems in molecular modeling, epidemiology, biomechanics, and computational physics. His work integrates graph neural networks, wavelet neural networks, periodic neural networks, and deep optimization frameworks to improve prediction accuracy and interpretability in molecular property analysis, virus transmission modeling, and dynamic system simulation. He has contributed significantly to AI-driven analysis of infectious diseases such as Marburg, Ebola, and Hepatitis C through nonlinear modeling, hybrid optimization, and neural simulation techniques. His research also advances computational mechanics, including deep learning-based vibration analysis in tapered beams, micro-electromechanical systems, and nonlinear oscillators. In cheminformatics, he has enhanced molecular property prediction using quantized GNNs, optimized activation functions, and improved training strategies for message-passing networks. His contributions extend to biophysical modeling, where neural networks simulate glycolysis, biochemical oscillators, and biological system dynamics with high fidelity. He has also explored angular softmax variations, Laplacian smoothing, and stochastic gradient methodologies in deep convolutional networks, advancing core theoretical aspects of machine learning. Across more than fifty publications, his work consistently integrates mathematical rigor with cutting-edge AI to address interdisciplinary scientific and engineering challenges.

 Profile:  Google Scholar

Featured Publications

Rahman, J. U., Noureen, I., Mannan, A., & Uwitije, R. (2025). PhyFold: Environment aware physics informed neural network for protein folding dynamics.

Ul Rahamn, J., Iqbal, M. A., Rasool, A., & Uwitije, R. (2025). p-GIN: A graph isomorphism network based on p-Laplacian operator to enhance molecular property prediction. Discover Applied Sciences, 7(11), 1251.

Shoket, N., Ul Rahman, J., & Zafar, N. (2025). Mathematical modeling and computational investigation of the COVID-19 epidemic using wavelet neural networks and coupled optimization. SN Soft Computing, 1–18.

Rahman, J. U., Ali, H., & Rassol, A. (2025). A comprehensive analysis of optimizers in message passing neural networks for molecular property prediction task. Computational Biology and Chemistry, 108556

Chaitanya Kumar Mankala | Machine Learning | Best Computer Engineering Award

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Dr. Chaitanya Kumar Mankala | Machine Learning
| Best Computer Engineering Award

Villanova University | United States

Dr. Chaitanya Kumar Mankala’s research focuses on advancing sustainable, scalable, and intelligent computing through the convergence of artificial intelligence, serverless architectures, and neuroidal network models. His work in real-time natural language processing emphasizes the development of energy-efficient and low-latency AI systems using cloud-native parallel processing on platforms such as AWS, enabling large-scale language models to operate dynamically with minimal environmental impact and operational cost. His contributions to evolutionary artificial neuroidal networks propose next-generation neural architectures capable of adaptively restructuring themselves through evolutionary algorithms to enhance learning efficiency, fault tolerance, and inference accuracy across diverse data environments. By integrating distributed serverless infrastructure with neuromorphic design principles, his research addresses limitations in current AI scalability, offering frameworks that support autonomous decision-making and real-time processing for healthcare, cybersecurity, and industrial automation. His conference work on the Next Generation Artificial Neural Network further explores biologically inspired computational models that bridge cognitive mechanisms with advanced deep learning, paving the way for highly interpretable, resilient, and self-evolving AI systems. Collectively, his research advances the paradigm of intelligent computing by integrating sustainability, scalability, and adaptive learning, contributing to the future of autonomous AI systems deployed on edge and cloud environments for mission-critical applications.

 Profile:  Orcid | Google Scholar 

Featured Publications

Mankala, C. K., & Silva, R. J. (2025). Sustainable real-time NLP with serverless parallel processing on AWS. Information, 16(10). https://doi.org/10.3390/info16100903

Mankala, C. K. (2025). Evolutionary artificial neuroidal network using serverless architecture [Doctoral dissertation].

Mankala, C. K., & Silva, R. (2023, November 16). Next generation artificial neural network. In Proceedings of the ICEHTMC Conference.

Shahina Begum | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Shahina Begum | Artificial Intelligence
| Best Researcher Award

Prof. Dr. Shahina Begum | Mallardalen University | Sweden

Prof. Dr. Shahina Begum, a Swedish national, is a distinguished Professor of Artificial Intelligence at the Artificial Intelligence and Intelligent Systems Research Group, Mälardalen University, Sweden, where she has been serving since 2019 after progressing through roles as Senior Lecturer  and Postdoctoral Researcher . She holds a Ph.D. ,Licentiate (2009), and Docent (2015) in Artificial Intelligence from Mälardalen University, as well as an M.Sc. in Computer Science (Intelligent Systems) from Dalarna University (2005). With over a decade of teaching and research experience, she has made significant contributions to AI through pioneering work in machine learning, intelligent decision support, and AI applications for health and well-being. Prof. Begum has demonstrated exceptional leadership in academia by securing substantial external research funding, amounting to approximately 224.4 MSEK as Principal Investigator and Co-PI, enabling groundbreaking multidisciplinary projects bridging AI with real-world applications. Her scholarly impact is reflected in her Google Scholar record, with an h-index of 30 and around 3,689 citations, and her Scopus record, with 88 indexed publications, 1,497 citations from 1,292 documents, and an h-index of 18. A passionate educator and mentor, she continues to inspire the next generation of AI researchers while actively contributing to the global AI research community through publications, collaborations, and leadership in funded projects.

 Profile: Scopus | Google Scholar | Linked IN | Staff Page | Orcid

Featured Publications

Islam, M. R., Ahmed, M. U., Barua, S., & Begum, S. (2022). A systematic review of explainable artificial intelligence in terms of different application domains and tasks. Applied Sciences, 12(3), 1353.

Begum, S., Ahmed, M. U., Funk, P., Xiong, N., & Folke, M. (2010). Case-based reasoning systems in the health sciences: A survey of recent trends and developments. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(2), 241–257.

Barua, S., Ahmed, M. U., Ahlström, C., & Begum, S. (2019). Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert Systems with Applications, 115, 121–135.

Chen, R. Y., Kung, V. L., Das, S., Hossain, M. S., Hibberd, M. C., Guruge, J., … & Begum, S. (2020). Duodenal microbiota in stunted undernourished children with enteropathy. New England Journal of Medicine, 383(4), 321–333.

Begum, S., Ahmed, M. U., Funk, P., Xiong, N., & Von Schéele, B. (2009). A case-based decision support system for individual stress diagnosis using fuzzy similarity matching. Computational Intelligence, 25(3), 180–195.

Degas, A., Islam, M. R., Hurter, C., Barua, S., Rahman, H., Poudel, M., Ruscio, D., … & Begum, S. (2022). A survey on artificial intelligence (AI) and explainable AI in air traffic management: Current trends and development with future research trajectory. Applied Sciences, 12(3), 1295.

Saqib Qamar | Artificial Intelligence | Best Researcher Award

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 Dr. Saqib Qamar |Artificial Intelligence
| Best Researcher Award

Assistant Professor at Sohar University, India.

Dr. Saqib Qamar is a dedicated Assistant Professor at Sohar University, Oman, specializing in computer science with expertise in medical image analysis and deep learning. With a Ph.D. from Huazhong University of Science and Technology, China, and postdoctoral research from Sweden’s top institutes—KTH and Umea University—he has demonstrated strong research capabilities through high-quality publications and international collaboration. His academic career spans teaching, curriculum development, and industry experience. Known for his work ethic, innovation, and research productivity, Dr. Qamar has a profound commitment to student success and scientific excellence. His scholarly contributions and interdisciplinary engagement make him a compelling candidate for the Best Researcher Award.

🌍 Professional Profile:

ORCID

Google Scholar

🏆 Suitability for the Best Researcher Award :

Dr. Saqib Qamar’s academic journey reflects consistent excellence and impactful contributions to computer science, particularly in medical image analysis using AI. With experience in global research environments and a record of peer-reviewed publications, he bridges theory with real-world applications. His Ph.D. work on 3D CNNs for brain MRI segmentation was both innovative and practically relevant. As a postdoctoral fellow at KTH and Umea University, he engaged in collaborative, cutting-edge research. He also actively mentors students and contributes to academic discourse. His awards, research leadership, and ongoing projects demonstrate a trajectory of influence, making him highly suitable for the Best Researcher Award.

🎓 Education :

Dr. Saqib Qamar earned his Ph.D. in Computer Science from Huazhong University of Science and Technology, China (2015–2019), with a dissertation focused on 3D CNN models for brain MRI segmentation. His doctoral research integrated deep learning with medical imaging and received the HUST Excellence Award. He completed his Master’s in Computer Science at Aligarh Muslim University, India (2010–2013), where he studied AI, programming, and software engineering, and was awarded a Merit Cum Means scholarship. His academic foundation was laid through a B.Sc. (Hons.) in Statistics from the same university (2007–2010), where he focused on probability, statistics, and linear algebra, graduating with First Division.

🏢 Work Experience :

Dr. Saqib Qamar is currently an Assistant Professor at Sohar University, Oman (2025–present), where he teaches and conducts research in AI and medical image computing. He was previously a postdoctoral fellow at KTH Royal Institute of Technology (2024–2025) and Umea University (2022–2024), Sweden, focusing on machine learning and deep learning applications. At Umea, he also taught deep learning courses. Earlier, he served as Assistant Professor at Madanapalle Institute of Technology and Science, India (2019–2021), where he taught programming and ML subjects. Prior to academia, he worked as a Database Developer at nServices, Delhi (2013–2015), specializing in Oracle-based data systems and programming.

🏅Awards and Honors

Dr. Saqib Qamar has been recognized for his academic performance and research excellence throughout his career. He received the HUST Excellence Award during his Ph.D. at Huazhong University of Science and Technology, acknowledging his exceptional work in medical image segmentation using deep learning. He was also awarded the Merit Cum Means Scholarship by the Government of India during his Master’s studies. His postdoctoral research in Sweden was supported through prestigious international fellowships, and he has contributed to multiple international projects. Additionally, he has received appreciation for teaching excellence and academic service in both India and Oman. His record of honors reflects his dedication to advancing AI and medical informatics research globally.

🔬 Research Focus :

Dr. Saqib Qamar’s research focuses on medical image analysis, deep learning, and 3D convolutional neural networks. His doctoral work centered on brain MRI segmentation, proposing efficient and parallelized CNN architectures. He has expanded his expertise during postdoctoral stints in Sweden, exploring advanced AI techniques in healthcare imaging, neural cell recognition, and explainable AI. His work integrates datasets from MRI and CT scans with machine learning algorithms to enhance diagnostic capabilities. He is also interested in real-time data processing, parallel computing, and interpretable AI models. With an aim to bridge clinical needs with computational innovation, Dr. Qamar’s research contributes significantly to the domains of health informatics and intelligent medical systems.

📊 Publication Top Notes:

📘 Techniques of data mining in healthcare: a review
🗓️ Year: 2015 | ✍️ P Ahmad, S Qamar, SQA Rizvi | 📖 International Journal of Computer Applications | 🔢 Cited by: 174 📊

🧠 A variant form of 3D-UNet for infant brain segmentation
🗓️ Year: 2020 | ✍️ S Qamar, H Jin, R Zheng, P Ahmad, M Usama | 📰 Future Generation Computer Systems | 🔢 Cited by: 132 🧬

🦴 CT-based automatic spine segmentation using patch-based deep learning
🗓️ Year: 2023 | ✍️ SF Qadri, H Lin, L Shen, M Ahmad, S Qadri, S Khan, M Khan, SS Zareen, S Qamar | 🧾 International Journal of Intelligent Systems | 🔢 Cited by: 77 🧠

🧠 Context aware 3D UNet for brain tumor segmentation
🗓️ Year: 2020 | ✍️ P Ahmad, S Qamar, L Shen, A Saeed | 📘 MICCAI Brainlesion Workshop | 🔢 Cited by: 57 🧪

🧬 MH UNet: A multi-scale hierarchical based architecture for medical image segmentation
🗓️ Year: 2021 | ✍️ P Ahmad, H Jin, R Alroobaea, S Qamar, R Zheng, F Alnajjar, F Aboudi | 📰 IEEE Access | 🔢 Cited by: 51 🔬

🧴 Dense encoder-decoder–based architecture for skin lesion segmentation
🗓️ Year: 2021 | ✍️ S Qamar, P Ahmad, L Shen | 🧠 Cognitive Computation | 🔢 Cited by: 50 🧪

🧠 HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation
🗓️ Year: 2021 | ✍️ S Qamar, P Ahmad, L Shen | 📘 Brainlesion: Glioma, MS, Stroke and TBI Workshop | 🔢 Cited by: 46 💡

Mohd Usama | Machine Learning | Best Researcher Award

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Assist. Prof. Dr. MohdUsama|MachineLearning
|Best Researcher Award

Postdoctoral Researcher at Umea University, Sweden Sweden.

Dr. Mohd Usama is a Postdoctoral Researcher at the Department of Diagnostics and Intervention, Umea University, Sweden. He holds a Ph.D. in Computer Science from Huazhong University of Science and Technology, China, focusing on deep learning for disease prediction and sentiment analysis. His research bridges artificial intelligence and medical imaging, particularly using GANs for domain adaptation and plaque detection in ultrasound imagery. With a solid teaching and research background across reputed institutions in India, he has significantly contributed to developing AI-based clinical decision support systems. His scholarly work, practical innovation, and interdisciplinary expertise make him highly suitable for the Best Researcher Award, exemplifying excellence in research, innovation, and educational service in the domains of biomedical engineering and artificial intelligence.



🌍 Professional Profile:


Scopus


Orcid


🏆 Suitability for the Best Researcher Award


 






































Dr. Mohd Usama exemplifies the qualities of a top-tier researcher through his impactful contributions to AI-driven medical imaging and clinical decision support systems. Currently a Postdoctoral Researcher at Umea University, Sweden, his work on generative adversarial networks for ultrasound-based atherosclerosis risk assessment addresses critical challenges in healthcare diagnostics. His strong academic foundation, interdisciplinary approach, and global research collaborations demonstrate exceptional innovation and dedication. Dr. Usama’s ability to translate deep learning research into real-world clinical applications, alongside a consistent record of teaching, publishing, and mentoring, positions him as a leader in his field. His scientific rigor, creativity, and societal impact make him a highly deserving candidate for the Best Researcher Award.






































🎓 Education 


Dr. Usama earned his Ph.D. in Computer Science from Huazhong University of Science and Technology, China (2016–2020), with a dissertation on “Recurrent Deep Learning for Text Processing with Application to Disease Prediction and Sentiment Analysis,” supervised by Prof. Min Chen. He completed his Master’s in Computer Science and Applications (71.78%, First Division) from Aligarh Muslim University (2013–2016), focusing on cloud-based electric vehicle charging management. His undergraduate degree is a B.Sc. (Hons) in Statistics (71.07%, First Division), also from Aligarh Muslim University (2009–2012), with a thesis on students’ opinions on the Ombudsman Bill in India. His academic journey reflects a blend of statistical foundations, computing applications, and interdisciplinary insights, crucial for innovative AI research in biomedical domains.


🏢 Work Experience 


Dr. Mohd Usama has served as a Postdoctoral Researcher at Umea University, Sweden (Dec 2022–Present), contributing to AI-powered clinical decision support systems and generative models for carotid ultrasound imaging. Previously, he worked as an Assistant Professor at the University of Petroleum and Energy Studies (2022), Kalasalingam Academy of Research and Education (2021–2022), and Madanapalle Institute of Technology and Science (2020–2021). He taught various courses including Deep Learning, Algorithms, Programming, and Information Security. His work spans both academia and research, with a deep engagement in curriculum development and applied machine learning. His experience in medical imaging research and teaching demonstrates a strong integration of theoretical and practical knowledge, making him a well-rounded and impactful scholar.


🏅 Awards and Honors 


Dr. Mohd Usama has been recognized for his innovative interdisciplinary research contributions at the intersection of artificial intelligence and healthcare. He received prestigious academic scholarships for his doctoral studies in China and earned consistent recognition throughout his academic career. He has been invited to deliver expert lectures and guest talks on AI, deep learning, and statistical computing at various institutions. His role in international collaborative projects on ultrasound imaging and disease prediction further demonstrates his global impact. As a frequent reviewer for reputed journals and contributor to academic forums, he maintains high standards of scholarly excellence. These achievements, coupled with his dedication to knowledge dissemination and impactful research, position him as a strong candidate for the Best Researcher Award.


🔬 Research Focus 
































































Dr. Mohd Usama’s research lies at the convergence of artificial intelligence, deep learning, and medical imaging. His work primarily involves the use of generative adversarial networks (GANs) to address domain adaptation, noise reduction, and feature interpolation in carotid ultrasound images. He develops AI-powered clinical decision support systems to enhance subclinical atherosclerosis risk prediction and ultrasound diagnostics. His doctoral research explored recurrent deep learning for text analysis in healthcare applications. He is also keenly interested in disease modeling, natural language processing, and sentiment analysis within clinical contexts. Dr. Usama’s work emphasizes real-world application of machine learning in healthcare, contributing to early diagnosis and precision medicine through robust, data-driven solutions, reinforcing his value as a research innovator.




























































































📊 Publication Top Notes:


    

  1. 

    Usama, M., Nyman, E., Näslund, U., & Grönlund, C. (2025).
    A domain adaptation model for carotid ultrasound: Image harmonization, noise reduction, and impact on cardiovascular risk markers.
    Computers in Biology and Medicine.
    https://doi.org/10.1016/j.compbiomed.2025.110030
    

    2. 

  2. 

    Usama, M., & Grönlund, C. (2023).
    Carotid Ultrasound Image Denoising Using Low-to-High Image Quality Domain Adaptation.
    The Medical Technology Days (MTD), 2023, Stockholm.
    

    3. 

  3. 

    Singh, A. P., Kumar, S., Kumar, A., & Usama, M. (2022).
    Machine Learning based Intrusion Detection System for Minority Attacks Classification.
    2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES).
    https://doi.org/10.1109/cises54857.2022.9844381
    

    4. 

  4. 

    Ahmad, B., Usama, M., Ahmad, T., Khatoon, S., & Alam, C. M. (2022).
    An ensemble model of convolution and recurrent neural network for skin disease classification.
    International Journal of Imaging Systems and Technology, 32(1), 15–24.
    https://doi.org/10.1002/ima.22661
    

    5. 

  5. 

    Ahmad, B., Usama, M., Huang, C. M., Hwang, K., Hossain, M. S., & Muhammad, G. (2020).
    Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network.
    IEEE Access, 8, 39098–39110.
    https://doi.org/10.1109/ACCESS.2020.2975198
    

    6. 

  6. 

    Qamar, S., Jin, H., Zheng, R., Ahmad, P., & Usama, M. (2020).
    A variant form of 3D-UNet for infant brain segmentation.
    Future Generation Computer Systems, 108, 618–628.
    https://doi.org/10.1016/j.future.2019.11.021
    

    7. 

  7. 

    Usama, M., Ahmad, B., Song, E., Hossain, M. S., Alrashoud, M., & Muhammad, G. (2020).
    Attention-based sentiment analysis using convolutional and recurrent neural network.
    Future Generation Computer Systems, 106, 336–347.
    https://doi.org/10.1016/j.future.2020.07.022
    

    8. 

  8. 

    Usama, M., Ahmad, B., Xiao, W., Hossain, M. S., & Muhammad, G. (2020).
    Self-attention based recurrent convolutional neural network for disease prediction using healthcare data.
    Computer Methods and Programs in Biomedicine, 187, 105191.
    https://doi.org/10.1016/j.cmpb.2019.105191
    

    9. 

  9. 

    Ahmad, P., Jin, H., Qamar, S., Zheng, R., Jiang, W., Ahmad, B., & Usama, M. (2019).
    3D Dense Dilated Hierarchical Architecture for Brain Tumor Segmentation.
    Proceedings of the 2019 4th International Conference on Big Data and Computing (ICBDC).
    https://doi.org/10.1145/3335484.3335516
    

    10. 

  10. 

    Ahmad, B., Usama, M., Lu, J., Xiao, W., Wan, J., & Yang, J. (2019).
    Deep Convolutional Neural Network Using Triplet Loss to Distinguish the Identical Twins.
    2019 IEEE Globecom Workshops (GC Wkshps).
    https://doi.org/10.1109/GCWkshps45667.2019.9024704
    

    11. 

  11. 

    Usama, M., Xiao, W., Ahmad, B., Wan, J., Hassan, M. M., & Alelaiwi, A. (2019).
    Deep Learning Based Weighted Feature Fusion Approach for Sentiment Analysis.
    IEEE Access, 7, 140361–140373.
    https://doi.org/10.1109/ACCESS.2019.2940051
    

    12. 

  12. 

    Usama, M., Ahmad, B., Yang, J., Qamar, S., Ahmad, P., Zhang, Y., Lv, J., & Guna, J. (2019).
    Equipping recurrent neural network with CNN-style attention mechanisms for sentiment analysis of network reviews.
    Computer Communications, 149, 111–121.
    https://doi.org/10.1016/j.comcom.2019.08.002
    

    13. 

  13. 

    Hao, Y., Usama, M., Yang, J., Hossain, M. S., & Ghoneim, A. (2019).
    Recurrent convolutional neural network based multimodal disease risk prediction.
    Future Generation Computer Systems, 98, 296–304.
    https://doi.org/10.1016/j.future.2018.09.031
    

    14. 


	


	

	






	
	

		


Elahe Karampour | Artificial Intelligence | Best Researcher Award
			

				Published on  by Engineering Scientist			

		
	


	

		
Ms. Elahe Karampour | Artificial Intelligence| Best Researcher Award


 


Ms. Elahe  Karampour  K.N.Toosi University Of Technology, Iran


Elahe Karampour is a dedicated researcher specializing in Geodesy and Geomatics Engineering with a focus on spatial-temporal data analysis and network science. She is currently pursuing her Master of Science at K.N. Toosi University of Technology, Tehran, Iran, with a thesis on analyzing location-based social networks using geometric curves. She has served as a research assistant, contributing to advancements in community detection and link prediction in spatial networks. Elahe is also an experienced educator, having taught GIS, spatial databases, and social network analysis. She has received national recognition for her academic excellence and was awarded a fully funded research visit to ScaDS.AI in Germany. Her expertise spans programming, spatial modeling, and AI-driven geospatial analytics.


🌍 Professional Profile:


Orcid


🏆 Suitability for the Best Researcher Award


 


Elahe Karampour is an outstanding researcher with significant contributions to geospatial data analysis, particularly in network science and location-based social networks. Her pioneering research integrates Ricci curvature and hyperbolic geometry for community detection and link prediction, leading to novel advancements in spatial-temporal modeling. She has published influential research in high-impact journals, demonstrating her expertise and innovation. Elahe’s research excellence is further recognized through a prestigious fully funded research visit to ScaDS.AI in Germany. With strong technical proficiency in R, Python, PostgreSQL, and GIS software, she bridges theory and application, developing intelligent recommender systems for urban planning. Her exceptional academic record, teaching excellence, and innovative research make her a prime candidate for the Best Researcher Award.


🎓 Education 


Elahe Karampour holds a Master of Science in Geodesy and Geomatics Engineering from K.N. Toosi University of Technology, Tehran, Iran (2021–2024). Her research focuses on spatial-temporal data analysis and social network modeling, with a thesis titled “Analysis of Location-Based Social Networks with Geometric Curves,” receiving a perfect grade of 20/20. She completed her Bachelor of Science in Geodesy and Geomatics Engineering at the University of Zanjan, Iran (2015–2020). Elahe ranked 23rd nationwide in the Iranian Master’s University Entrance Exam, showcasing her academic excellence. Her strong analytical and technical skills, coupled with expertise in GIS, AI, and network analysis, enable her to make significant contributions to the field of geospatial research and urban data science.


🏢 Work Experience 


Elahe Karampour has extensive research and teaching experience in geospatial data analysis and network science. As a Research Assistant (2022–2024) at K.N. Toosi University of Technology, she developed advanced models for community detection and link prediction in location-based social networks using geometric methods. She also worked as a Lecturer (2024) at Babol Noshirvani University of Technology, teaching spatial analysis and visualization to undergraduate students. Additionally, she served as a Teaching Assistant (2023–2024), guiding master’s and PhD students in GIS, social network analysis, and spatial databases. Her technical expertise in R, Python, PostgreSQL, and QGIS, combined with her ability to integrate AI with geospatial analysis, has positioned her as a leader in her research domain.


🏅 Awards and Honors 


Elahe Karampour has received multiple accolades for her academic and research excellence. She ranked 23rd nationwide in the Iranian Master’s University Entrance Exam (2021), demonstrating her strong academic foundation. She was awarded a fully funded research grant for a short-term visit to ScaDS.AI Center for Scalable Data Analytics and Artificial Intelligence in Germany (2023), recognizing her contributions to AI-driven geospatial analysis. Additionally, she was listed among the top-ranked teachers by her students for her exceptional teaching performance. Her work in network science and geospatial modeling has led to publications in high-impact journals, further cementing her status as a leading researcher. These achievements underscore her dedication and outstanding contributions to the field of geospatial and network science.


🔬 Research Focus 




















Elahe Karampour’s research centers on spatial-temporal data analysis, network science, and AI-driven geospatial modeling. She specializes in analyzing location-based social networks using advanced mathematical frameworks such as Ricci curvature and hyperbolic geometry for community detection and link prediction. Her work integrates graph-based modeling with GIS technologies to enhance urban planning, mobility analysis, and personalized recommender systems. Elahe has developed innovative approaches to analyzing complex data structures, utilizing machine learning and AI techniques for geospatial applications. She is particularly interested in the intersection of mathematics, computer science, and geospatial technologies, aiming to create data-driven solutions for urban analytics and smart city development. Her research has been recognized internationally, reinforcing her expertise in geospatial data science.




























📊 Publication Top Notes:


    

  • 

    Karampour, E., Malek, M.R., & Eidi, M. (2025). Discrete Ricci flow: A powerful method for community detection in location-based social networks. Computers and Electrical Engineering, 123, 110302.
    

    

    •