Tomohiro Hayashida | Machine Learning | Best Researcher Award

Published on by Engineering Scientist

Prof. Tomohiro Hayashida | Machine Learning
| Best Researcher Award

Professor at Hiroshima University , Japan.

Professor Tomohiro Hayashida is a distinguished scholar in decision-making, machine learning, and optimization, currently serving at Hiroshima University. After earning his Master’s and Ph.D. in Engineering from the same institution, he joined the university as a Research Associate in 2006 and steadily rose to Full Professor by 2024. With over 80 academic publications, Prof. Hayashida has led competitive national research grants and worked on practical innovations in transportation and scheduling algorithms. His interdisciplinary collaborations span across academia and industry, reflecting his commitment to both theoretical advancements and real-world applications. His citation record, leadership in JSPS-funded projects, and role in applied AI solutions exemplify his deep impact in computational engineering and operations research.

🌍 Professional Profile:

Scopus

🏆 Suitability for the Best Researcher Award :

Prof. Tomohiro Hayashida exemplifies the qualities deserving of the Best Researcher Award. He has produced over 50 peer-reviewed journal papers, many in top-tier SCI/Scopus-indexed journals. As Principal Investigator of multiple JSPS KAKEN-funded projects, including those in evolutionary computing and dynamic systems, he has shown consistent research leadership. His collaborative work with industry, such as optimizing dispatch algorithms with SmartRyde Inc., demonstrates strong translational research. With an h-index around 11–12 and 442+ citations, he balances scholarly excellence and societal impact. His active role in multi-disciplinary collaborations—both domestic and international—further reinforces his stature as an innovative and impactful researcher, making him highly suitable for the Best Researcher Award.

🎓 Education :

Prof. Hayashida received his entire higher education from Hiroshima University. He completed his Master’s degree in Engineering in 2006 and subsequently pursued and earned his Ph.D. in Engineering. His academic training focused on optimization theory, intelligent systems, and operations research, equipping him with a robust foundation in both theoretical and applied research. The university’s focus on computational intelligence and engineering sciences helped shape his research vision early in his career. His seamless transition from student to researcher within the same academic institution showcases his consistent excellence and growth as a scholar. This strong academic grounding laid the basis for his long-term contributions to machine learning, decision sciences, and interdisciplinary engineering research.

🏢 Work Experience :

Prof. Tomohiro Hayashida began his academic career in 2006 as a Research Associate at Hiroshima University, immediately after earning his Master’s degree. He was promoted to Assistant Professor in 2007, Associate Professor in 2015, and Full Professor in 2024. He has over 18 years of academic experience in teaching, research, and supervision. Beyond academic duties, he is active in government-funded research and industry collaborations, such as the ride-hailing optimization project with SmartRyde Inc. He also contributes to the Digital Manufacturing Education and Research Center at Hiroshima University. His extensive experience in both research project leadership and educational innovation showcases a balanced, impactful academic career with national and international influence.

🏅Awards and Honors

While specific award titles are not publicly listed, Prof. Hayashida’s selection as Principal Investigator for multiple highly competitive JSPS KAKEN Grants—including Young Researcher awards and Scientific Research (C) projects—reflects significant national recognition of his research excellence. His promotion to Full Professor at Hiroshima University, a top-tier Japanese institution, itself is a mark of academic distinction. He has been entrusted with strategic roles in collaborative projects, some of which have gained media coverage, such as the SmartRyde dispatch algorithm. These achievements, combined with a strong citation record and presence in high-impact journals, serve as implicit acknowledgment of his contributions to AI, optimization, and applied decision-making sciences within both academic and practical domains.

🔬 Research Focus :

Prof. Hayashida’s research centers on decision-making, machine learning, optimization, and evolutionary computation. His work addresses complex real-world problems like multi-objective scheduling, group decision analysis, and dynamic system optimization. Through JSPS-funded projects, he has developed algorithms for adaptive agents, cooperative enterprises, and evolutionary scheduling. His recent collaborations include intelligent systems for ride-hailing and dynamic dispatching. He integrates mathematical modeling with practical applications, focusing on AI-driven solutions for industries such as energy systems and transportation. His interdisciplinary approach merges operations research, computer science, and systems engineering. With over 80 publications and national/international partnerships, his research advances both academic knowledge and technological innovation, particularly in adaptive, data-driven decision systems.

📊 Publication Top Notes:

📘 Integrated Optimization Method for Task Allocation and Hierarchical Reinforcement Learning in Cargo Transport Robots
🗓️ Year: 2025 | 📚 Journal: IEEJ Transactions on Electronics Information and Systems |

📄 Constrained-multiobjective Evolutionary Algorithm for Distribution System Reconfiguration under Severe Constraints
🗓️ Year: 2025 | 📚 Conference Paper |

🚚 Integrating Task Allocation and Hierarchical Reinforcement Learning for Optimized Cargo Transport Routing
🗓️ Year: 2025 | 📚 Conference Paper |

⚡ Distribution System Reconfiguration by an Evolutionary Algorithm using Constraint-Guided Dominance and Archive-Based Individual Preservation Strategy
🗓️ Year: 2024 | 📚 IEEJ Transactions on Power and Energy |

📊 Expectation and Fractile Models for Decentralised Distribution Systems under Demand Uncertainty and their Computational Methods
🗓️ Year: 2024 | 📚 International Journal of Operational Research |

🎓 WIP: Machine Learning Models for Predicting Student Performance in IoT-Enhanced Education
🗓️ Year: 2024/2025 | 📚 Conference Paper |

📈 WIP: Study on a Data-Driven Adaptive Learning Support System Design for Individualized Optimal Learning
🗓️ Year: 2024/2025 | 📚 Conference Paper |

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.
    

    

    •