Minh-Son Dao | Deep Learning | Best Researcher Award

Published on by Computer Vision Awards

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.

Shujiao Liao | Machine Learning | Best Researcher Award

Published on by Computer Vision Awards

Prof . Shujiao Liao | Machine Learning | Best Researcher Award

Professor at Minnan Normal University, China

Dr. Shujiao Liao is a full professor at the School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, Fujian, China. With a strong academic background in applied mathematics and software engineering, she has dedicated her career to advancing the fields of granular computing, data mining, and machine learning. Her work bridges theoretical mathematics and computational methodologies, enabling novel approaches to intelligent data analysis. Over the years, Dr. Liao has played a pivotal role in both academic teaching and research leadership, contributing significantly to her institution’s development and scholarly output. She has guided numerous students and collaborated across interdisciplinary research groups. Her commitment to innovation and academic excellence makes her a respected figure in her field. As a scholar deeply engaged in cutting-edge technologies and data science trends, she continues to contribute impactful research and strives to address complex problems with analytical precision and computational insight.

Professional Profile 

Education🎓

Dr. Shujiao Liao holds a strong interdisciplinary educational background that underpins her academic career. She earned her Master of Science degree in Applied Mathematics from Shantou University, Guangdong, China, in 2006, where she built a solid foundation in mathematical modeling and analytical reasoning. Her pursuit of advanced studies led her to obtain a Ph.D. degree in Software Engineering from the University of Electronic Science and Technology of China, Chengdu, Sichuan, in 2018. This advanced degree enabled her to integrate mathematical theory with practical software systems, contributing to her versatility in computational research. Her doctoral studies focused on bridging data-centric algorithms with intelligent systems, which now form the core of her research interests. This rich educational trajectory has allowed her to approach complex scientific questions from both a mathematical and engineering perspective, making her academic contributions particularly robust in the fields of data mining and machine learning.

Professional Experience📝

Dr. Shujiao Liao is currently a full professor at the School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, Fujian, China. With an academic career that spans over a decade, she has demonstrated excellence in teaching, research, and academic leadership. In her current role, she teaches advanced mathematics and computational theory courses, supervises postgraduate research projects, and actively engages in departmental development. She has led several internal and collaborative research initiatives in granular computing and machine learning, working closely with both academic and industrial partners. Her experience also includes conference presentations, curriculum development, and cross-disciplinary project coordination. She is recognized for her effective mentorship, contributing to the growth of young researchers and promoting high standards in academic inquiry. Through her consistent professional contributions, Dr. Liao has helped elevate her institution’s research standing and continues to serve as a vital resource for the academic community in mathematics and software research.

Research Interest🔎

Dr. Shujiao Liao’s research interests span several pivotal domains in computer science and applied mathematics, with a particular focus on granular computing, data mining, and machine learning. Her work in granular computing explores how knowledge can be structured and processed using information granules, improving the interpretability and efficiency of decision-making systems. In the area of data mining, she investigates algorithms for pattern discovery, classification, and clustering, contributing to improved data-driven strategies in scientific and industrial applications. Her interests in machine learning include developing intelligent models capable of adaptive learning and robust performance across complex datasets. Dr. Liao’s research bridges theory and application, aiming to solve real-world problems such as intelligent diagnostics, automated reasoning, and big data analysis. Her interdisciplinary focus allows her to work on innovative projects that combine mathematical rigor with computational techniques, positioning her as a contributor to the evolving field of intelligent systems and artificial intelligence.

Award and Honor🏆

While specific awards and honors for Dr. Shujiao Liao were not provided in the given information, her appointment as a full professor reflects recognition of her academic contributions and research leadership. Attaining such a role typically involves competitive peer-reviewed evaluations, consistent scholarly output, and excellence in teaching and mentorship. It is likely that she has received internal university-level commendations, research project funding awards, or participation in prestigious academic panels, common among professors of her standing. If available, details such as Best Paper Awards, Research Excellence Awards, or National Science Grants would further highlight her academic acclaim. Her long-standing role in the academic community and sustained focus on impactful research suggest she is a strong candidate for further honors at national or international levels. Formal acknowledgment through such accolades would complement her already impressive academic and research credentials, reinforcing her eligibility for broader recognitions such as the Best Researcher Award.

Research Skill🔬

Dr. Shujiao Liao possesses a robust set of research skills grounded in both theoretical understanding and practical application. She demonstrates strong expertise in mathematical modeling, algorithm development, and data analysis, which are essential for her work in granular computing and data mining. Her proficiency in applying machine learning techniques to complex datasets enables her to design predictive models with real-world relevance. She is adept at academic writing, literature review, and hypothesis-driven exploration, essential for high-quality publications and grant writing. Additionally, Dr. Liao has strong collaborative and project management skills, allowing her to lead interdisciplinary research teams and coordinate joint research initiatives. Her experience in supervising graduate theses further reflects her ability to guide rigorous research methodologies. She is also likely skilled in programming languages and tools used in data science, such as Python, MATLAB, or R, further supporting her contributions to computational research domains.

Conclusion💡

Dr. Shujiao Liao is a strong candidate for the Best Researcher Award, particularly within fields like granular computing and machine learning. Her academic background and full professorship position suggest a high level of expertise and leadership. To solidify her candidacy for top-tier recognition, showcasing quantifiable research outcomes, international influence, and broader impact will be important.

Publications Top Noted✍

  • Title: WrdaGAN: A text-to-image synthesis pipeline based on Wavelet Representation and Adaptive Sample Domain Constraint strategy
    Authors: Yongchao Qiao, Ya’nan Guan, Shujiao Liao, Wenyuan Yang, Weiping Ding, Lin Ouyang
    Year: 2025
    Citation: DOI: 10.1016/j.engappai.2025.111305

  • Title: Semisupervised Feature Selection With Multiscale Fuzzy Information Fusion: From Both Global and Local Perspectives
    Authors: Nan Zhou, Shujiao Liao, Hongmei Chen, Weiping Ding, Yaqian Lu
    Year: 2025
    Citation: DOI: 10.1109/TFUZZ.2025.3540884

  • Title: S-approximation spaces extension model based on item-polytomous perspective
    Authors: Xiaojie Xie, Shujiao Liao, Jinjin Li
    Year: 2024
    Citation: DOI: 10.21203/rs.3.rs-4447331/v1

  • Title: Multi-Target Rough Sets and Their Approximation Computation with Dynamic Target Sets
    Authors: Wenbin Zheng, Jinjin Li, Shujiao Liao
    Year: 2022
    Citation: DOI: 10.3390/info13080385

  • Title: Multi-Label Attribute Reduction Based on Neighborhood Multi-Target Rough Sets
    Authors: Wenbin Zheng, Jinjin Li, Shujiao Liao, Yidong Lin
    Year: 2022
    Citation: DOI: 10.3390/sym14081652

  • Title: Attribute‐scale selection for hybrid data with test cost constraint: The approach and uncertainty measures
    Authors: Shujiao Liao, Yidong Lin, Jinjin Li, Huiling Li, Yuhua Qian
    Year: 2022
    Citation: DOI: 10.1002/int.22678

  • Title: Feature–granularity selection with variable costs for hybrid data
    Authors: Shujiao Liao, Qingxin Zhu, Yuhua Qian
    Year: 2019
    Citation: DOI: 10.1007/s00500-019-03854-2