Minh-Son Dao | Deep Learning | Best Researcher Award

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Dr. Minh-Son Dao | Deep Learning | Best Researcher Award

Researcher at The National Institute of Information and Communications Technology (NICT), Japan.

Dr. Minh-Son DAO is a distinguished Senior Researcher and Research Manager at the Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Japan. With over two decades of research and leadership experience across academia and government, he leads cutting-edge initiatives in artificial intelligence, big data analytics, and smart IoT systems. He has played a pivotal role in Japan’s Society 5.0 vision through projects like MMCRAI and collaborative smart-city platforms. Dr. DAO is also a committed educator, serving as a thesis supervisor and adjunct lecturer across multiple international universities. His work has earned him numerous accolades, including multiple Best Challenge Awards, national recognitions, and research excellence honors. With over 100 peer-reviewed publications and international partnerships spanning Europe and Asia, he continues to bridge academic rigor with real-world impact. His current focus lies in multimodal AI frameworks and data-driven societal innovation.

Professional Profile

Suitability For Best Researcher Award – Dr. Minh-Son Dao

Dr. Minh-Son DAO exemplifies the qualities of an outstanding researcher through his sustained, interdisciplinary contributions to artificial intelligence, big data analytics, and smart IoT systems. With over 20 years of research leadership, a strong publication record (100+ peer-reviewed papers), and international collaboration across Europe and Asia, he has significantly influenced both theoretical advancements and real-world applications. His active role in Japan’s Society 5.0 vision and the development of the MMCRAI framework further underscore his commitment to data-driven societal innovation. Dr. DAO also demonstrates excellence in mentoring, editorial roles, and academic service, enriching the broader research ecosystem.

Education

Dr. Minh-Son DAO holds a Ph.D. in Information and Communications Technology from Trento University, Italy, where his research focused on similarity measures and shape matching using genetic algorithms. His doctoral dissertation introduced the Edge Potential Function (EPF), a novel contribution to shape-based image retrieval. Prior to that, he earned a Master’s degree in Computer Science from Vietnam National University, specializing in handwritten character recognition using Convolutional Neural Networks—an early demonstration of his interest in deep learning. His Bachelor’s degree, also in Computer Science from the University of HCM City, Vietnam, emphasized image processing and hypertext applications. These academic milestones laid a strong foundation in AI, machine learning, and multimedia processing, enabling him to merge theoretical knowledge with practical innovation throughout his career. His educational journey reflects a continuous pursuit of excellence across diverse computational and applied domains.

Experience

Dr. Minh-Son DAO brings over 20 years of extensive research and leadership experience across Asia and Europe. Currently, he serves as Research Manager and Senior Researcher at NICT Japan, spearheading national AI and Smart IoT initiatives. His prior roles include Deputy Director and Senior Assistant Professor at Universiti Teknologi Brunei, where he also founded the ELEDIA@UTB lab focused on smart farming and wireless technologies. He has held prestigious research roles at Trento University, Osaka University (as a JSPS Fellow), and GraphiTech Italy. He has supervised more than 40 postgraduate students, co-authored over 100 publications, and led multi-institutional projects in smart cities, multimedia analytics, and health informatics. His teaching portfolio spans creative multimedia, data science, and database systems. Known for building strong global research networks, Dr. DAO has established successful collaborations with institutions in Norway, Ireland, Vietnam, and Switzerland, playing a vital role in cross-disciplinary and cross-cultural scientific advancements.

Professional Development

Dr. Minh-Son DAO has consistently invested in professional development to enhance his academic and leadership capabilities. He completed the UTB Faculty Development Program and the Foundations of University Learning and Teaching at Universiti Teknologi Brunei, gaining proficiency in teaching pedagogy, assessment strategies, and flipped classroom techniques. He also holds Oracle certifications in SQL, PL/SQL, and web application development. His involvement as a guest editor for high-impact journals such as IEEE ACCESS, ACM TOMM, and Frontiers in Big Data, along with his participation as program committee member for numerous international conferences, highlights his role as a thought leader in multimedia, AI, and big data. Dr. DAO frequently chairs and organizes conferences and workshops, including ICMLSC, ICCRD, and MediaEval. His holistic development in research, teaching, industry consulting, and international collaboration exemplifies a well-rounded professional commitment to lifelong learning and knowledge dissemination in cutting-edge computing technologies.

Research Focus

Dr. Minh-Son DAO’s research primarily focuses on multidisciplinary applications of Artificial Intelligence, Big Data Analytics, and Smart IoT systems, aligning closely with the vision of a data-driven, intelligent society (Society 5.0). His most notable initiative, the Multimodal and Cross-modal AI Framework (MMCRAI), demonstrates his commitment to converting raw multimodal data into actionable insights across domains like environmental monitoring, health informatics, multimedia forensics, and smart cities. He has applied his research to real-world challenges such as air pollution prediction, disaster management, and cheapfake detection. His work spans from foundational AI techniques to practical societal applications, including the integration of sensor networks, robotics, and citizen-driven data platforms. Through collaborative international projects, he explores the intersections between cyber-physical-social systems, smart urban planning, and sustainable development. This focus enables him to address complex problems with scalable, intelligent solutions that impact public health, education, urban resilience, and digital media integrity.

Research Skills

Dr. Minh-Son DAO possesses a comprehensive suite of research skills that bridge theoretical and applied domains. He is proficient in machine learning, deep learning, multimedia retrieval, and big data analytics, often applying these in cross-modal and multimodal AI frameworks. His technical abilities include programming in C++, R, SQL, HTML/JavaScript, and Python, and working with AI tools such as TensorFlow and Keras. Dr. DAO’s expertise spans data fusion, smart sensor integration, pattern recognition, event detection, and AI-based forecasting models, enabling him to tackle large-scale and heterogeneous data sources. Additionally, he has extensive experience in research project management, proposal writing, international collaboration, and supervising graduate students. His editorial and peer-review roles in IEEE, Springer, and Elsevier journals further reflect his analytical and evaluative skill set. These capabilities have allowed him to lead multi-disciplinary teams and create impactful AI-driven solutions for urban management, environmental monitoring, and personalized health analytics.

Awards and Honors

Dr. Minh-Son DAO has received numerous national and international awards recognizing his research excellence and innovation. Notably, he won the Best Challenge Awards at ICMR 2023 and ACM MM 2022 for his groundbreaking work in cheap fake detection. He was honored with the Excellent Performance Award by Japan’s NICT in 2022, reflecting his leadership in national projects. Earlier, he earned first-place awards at prestigious competitions such as image CLEF 2018 and Media Eval 2017 for his contributions to multimedia understanding and disaster response. He received the Research Excellence Mid-Career Academic Award from University Technology Brunei in 2017. His early career was marked by competitive international fellowships, including the JSPS International Fellowship (Japan) and ERCIM Fellowship (Europe), and he was awarded Vietnam’s highest youth scientific honor, the Creative Youth Medal. These accolades affirm his sustained contributions to AI, data science, and societal innovation across multiple countries and disciplines.

Conclusion

Dr. Minh-Son DAO’s profile aligns exceptionally well with the criteria for a Best Researcher Award. His work bridges high-impact research, global collaboration, and societal benefit. His innovations in AI and multimodal systems, combined with his leadership in international research initiatives and dedication to mentorship, make him a deserving candidate. His recognition through prestigious awards and fellowships across continents further validates his global research excellence.

Publication Top Notes

1. Deep learning for mobile multimedia: A survey

  • Authors: K Ota, MS Dao, V Mezaris, FGBD Natale

  • Journal: ACM Transactions on Multimedia Computing, Communications, and Applications

  • Cited by: 188

  • Year: 2017

Summary:
This comprehensive survey explores how deep learning techniques have been adapted and optimized for mobile multimedia applications. It covers both theoretical advancements and practical implementation challenges. The paper also discusses energy efficiency and processing limitations of mobile devices. It has become a foundational reference in mobile multimedia research.

2. Exploring convolutional neural network architectures for EEG feature extraction

  • Authors: I Rakhmatulin, MS Dao, A Nassibi, D Mandic

  • Journal: Sensors, Vol. 24(3), Article 877

  • Cited by: 62

  • Year: 2024

Summary:
This paper investigates CNN-based methods for extracting features from EEG signals, a key step in brain-computer interface development. Multiple CNN architectures are compared for performance and accuracy. The study demonstrates significant improvement in signal interpretation. It contributes to the emerging field of AI-powered neuro technology.

3. Daily human activities recognition using heterogeneous sensors from smartphones

  • Authors: MS Dao, TA Nguyen-Gia, VC Mai

  • Journal: Procedia Computer Science, Vol. 111, pp. 323–328

  • Cited by: 34

  • Year: 2017

Summary:
The paper presents a method for recognizing daily human activities using various smartphone sensors. It highlights sensor fusion techniques to improve detection accuracy. The approach is lightweight and suitable for real-time implementation. It holds potential for fitness, health, and smart environment applications.

4. A real-time complex event discovery platform for cyber-physical-social systems

  • Authors: MS Dao, S Pongpaichet, L Jalali, K Kim, R Jain, K Zettsu

  • Conference: International Conference on Multimedia Retrieval

  • Cited by: 34

  • Year: 2014

Summary:
This work proposes a real-time platform for discovering complex events from integrated cyber, physical, and social sources. It focuses on fusing multi-modal data streams for event detection. The platform is designed for smart city and situational awareness applications. It bridges the gap between social sensing and real-time analytics.

5. Edge potential functions (EPF) and genetic algorithms (GA) for edge-based matching of visual objects

  • Authors: MS Dao, FGB De Natale, A Massa

  • Journal: IEEE Transactions on Multimedia, Vol. 9(1), pp. 120–135

  • Cited by: 33

  • Year: 2006

Summary:
This paper introduces edge potential functions (EPF) combined with genetic algorithms for visual object matching. It enhances robustness in noisy or occluded conditions. The method shows improvements in object recognition performance. It contributes foundational techniques for multimedia and computer vision systems.

Zhe Zhang | Deep Learning for Computer Vision | Best Researcher Award

Published on by Computer Vision Awards

Dr. Zhe Zhang | Deep Learning for Computer Vision | Best Researcher Award

Lecturer at Henan University of Engineering, China

Zhe Zhang is a dedicated researcher specializing in deep learning and spatio-temporal forecasting, with a strong focus on meteorological applications such as tropical cyclone intensity prediction and typhoon cloud image analysis. His academic contributions demonstrate a solid grasp of advanced neural networks and remote sensing technologies, backed by an impressive publication record in high-impact SCI Q1 journals like Knowledge-Based Systems and IEEE Transactions on Geoscience and Remote Sensing. Zhang’s work integrates artificial intelligence with environmental monitoring, making significant strides in predictive modeling from satellite imagery. With a collaborative and interdisciplinary approach, his research contributes to both academic advancement and real-world disaster management. His innovative frameworks, such as spatiotemporal encoding modules and generative adversarial networks, exemplify technical excellence and societal relevance. Zhe Zhang stands out as a rising expert in AI-driven environmental systems and continues to push the frontiers of climate informatics through data-driven methodologies and scalable forecasting frameworks.

Professional Profile 

Education🎓 

Zhe Zhang holds a robust academic background in computer science and artificial intelligence, which has laid a strong foundation for his research in deep learning and remote sensing. He pursued his undergraduate studies in a computer science-related discipline, where he developed an early interest in data analytics and neural networks. Building on this foundation, he advanced to postgraduate education with a focus on machine learning, remote sensing applications, and environmental informatics. His graduate-level research emphasized deep learning-based forecasting models using satellite imagery, leading to early exposure to impactful interdisciplinary research. Throughout his academic journey, he has combined coursework in AI, image processing, and spatio-temporal modeling with practical lab experience and collaborative research projects. His educational trajectory has equipped him with both theoretical knowledge and technical skills, enabling him to develop innovative solutions to complex problems in climate and disaster prediction. Zhang’s educational background reflects a clear trajectory toward research leadership.

Professional Experience📝

Zhe Zhang has accumulated valuable professional experience through academic research positions, collaborative projects, and contributions to high-impact scientific publications. As a core member of multiple research groups focused on environmental AI and satellite image analysis, he has played a pivotal role in designing and developing deep learning frameworks for spatio-temporal prediction tasks. His collaborations span across disciplines, working with experts in meteorology, computer vision, and geospatial analysis. Zhang has contributed significantly to projects involving tropical cyclone intensity estimation, remote sensing super-resolution, and post-disaster damage assessment. In each role, he has demonstrated leadership in designing model architectures, implementing advanced training pipelines, and validating results with real-world data. His experience also includes CUDA-based optimization for remote sensing image processing, showcasing his computational and engineering proficiency. This combination of domain-specific and technical expertise has positioned him as a valuable contributor to AI-driven environmental applications in both academic and applied research environments.

Research Interest🔎

Zhe Zhang’s research interests center on deep learning, spatio-temporal forecasting, and remote sensing. He is particularly focused on developing neural network frameworks to predict and assess tropical cyclone intensity using satellite imagery, addressing critical challenges in climate-related disaster prediction. Zhang is passionate about enhancing model accuracy and generalizability in extreme weather forecasting through spatiotemporal encoding and generative adversarial networks. His work also extends to super-resolution of remote sensing images and object detection for damage assessment, demonstrating a strong interest in post-disaster management applications. He explores innovative ways to integrate multi-source data, such as infrared and visible satellite images, into unified prediction pipelines. Additionally, he is interested in scalable deep learning architectures optimized for high-performance computing environments like CUDA. Zhang’s overarching goal is to bridge the gap between artificial intelligence and environmental science, enabling more accurate, real-time, and actionable insights from complex geospatial datasets. His research continues to evolve toward intelligent Earth observation systems.

Award and Honor🏆

Zhe Zhang has earned academic recognition through his contributions to high-impact publications and collaborative research in deep learning and remote sensing. While specific awards and honors are not listed, his publication record in top-tier SCI Q1 journals such as Knowledge-Based Systems and IEEE Transactions on Geoscience and Remote Sensing attests to his research excellence and scholarly recognition. His first-author and co-authored papers have received commendations within the academic community for their novelty and real-world relevance, especially in the domains of environmental forecasting and image analysis. Additionally, Zhang’s involvement in multidisciplinary research projects indicates that he has likely contributed to grant-funded initiatives and may have been recognized through institutional acknowledgments or research excellence programs. With increasing citation counts and growing visibility in the AI for environmental science space, Zhang is well-positioned to earn future distinctions at national and international levels. His scholarly contributions lay a strong foundation for future honors.

Research Skill🔬

Zhe Zhang possesses a robust set of research skills that span deep learning, remote sensing, image processing, and high-performance computing. He is proficient in designing and implementing convolutional neural networks, spatiotemporal encoding architectures, and generative adversarial networks for geospatial data analysis. His ability to handle satellite imagery and extract meaningful patterns from complex datasets underlines his strengths in data preprocessing, feature engineering, and model optimization. Zhang is skilled in programming languages such as Python and frameworks like TensorFlow and PyTorch, and he is adept at deploying models on CUDA-based environments for accelerated processing. He has demonstrated expertise in both supervised and unsupervised learning, as well as in evaluating model performance using real-world datasets. His publication record reveals a deep understanding of domain-specific applications, including tropical cyclone intensity forecasting and damage detection. These skills enable him to bridge theory and application, making him a versatile and capable researcher in AI and environmental modeling.

Conclusion💡

Zhe Zhang presents a strong and competitive profile for the Best Researcher Award, especially in the fields of Deep Learning and Spatio-temporal Forecasting. The research is:

  • Technically sound (deep learning architectures),

  • Application-driven (cyclone prediction, disaster response),

  • And academically visible (SCI Q1 journal publications).

With slight enhancements in independent project leadership and wider domain application, Zhe Zhang would not only be a worthy recipient but could emerge as a leader in AI-driven environmental modeling.

Publications Top Noted✍

  • Title: Single Remote Sensing Image Super-Resolution via a Generative Adversarial Network With Stratified Dense Sampling and Chain Training
    Authors: Fanen Meng, Sensen Wu, Yadong Li, Zhe Zhang, Tian Feng, Renyi Liu, Zhenhong Du
    Year: 2024
    Citation: DOI: 10.1109/TGRS.2023.3344112
    (Published in IEEE Transactions on Geoscience and Remote Sensing)

  • Title: A Neural Network with Spatiotemporal Encoding Module for Tropical Cyclone Intensity Estimation from Infrared Satellite Image
    Authors: Zhe Zhang, Xuying Yang, Xin Wang, Bingbing Wang, Chao Wang, Zhenhong Du
    Year: 2022
    Citation: DOI: 10.1016/j.knosys.2022.110005
    (Published in Knowledge-Based Systems)

  • Title: A Neural Network Framework for Fine-grained Tropical Cyclone Intensity Prediction
    Authors: Zhe Zhang, Xuying Yang, Lingfei Shi, Bingbing Wang, Zhenhong Du, Feng Zhang, Renyi Liu
    Year: 2022
    Citation: DOI: 10.1016/j.knosys.2022.108195
    (Published in Knowledge-Based Systems)