Huawen Liu | Machine Learning | Distinguished Scientist Award

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Prof. Dr. Huawen Liu | Machine Learning | Distinguished Scientist Award

Professor at Shaoxing University, China

Prof. Huawen Liu 👨‍🏫, a distinguished academic at Shaoxing University 🇨🇳 since 2010, holds a Ph.D. and Ms.D. in Computer Science from Jilin University 🧠💻. He expanded his research globally as a postdoc at the University of South Australia 🇦🇺 (2012–2013) and a visiting fellow at the University of Texas at San Antonio 🇺🇸 (2018–2019). His work spans hash learning, AI, big data, and machine learning 🤖📊. With over 50 publications 📚 in top-tier journals, he actively contributes as an editor and conference organizer. He holds an H-index of 17 📈 and continues to shape intelligent computing worldwide 🌐.

Professional Profile:

Google Scholar

Scopus

🎓 Education & Experience 

  • 🎓 Ph.D. & Ms.D. in Computer Science from Jilin University, China 🇨🇳 (Top-10 university)

  • 🧑‍🔬 Postdoctoral Researcher at University of South Australia 🇦🇺 (2012–2013)

  • 🌍 Visiting Fellow at University of Texas at San Antonio, USA 🇺🇸 (2018–2019)

  • 👨‍🏫 Professor at Shaoxing University since July 2010 🏫

  • 📝 Over 50 peer-reviewed publications in high-impact journals and conferences 📚

🌱 Professional Development 

Prof. Liu has actively participated in shaping the research community 🌐. He serves as the Editor-in-Chief (EIC) of the International Journal of Intelligence and Sustainable Computing 🧠💡, Associate Editor for International Journal of Artificial Intelligence and Tools 🛠️, and Editor for Mathematics ➗📘. He has also led special issues as Guest Editor in Neural Computing and Applications 🧮 and Computing and Informatics 💻. His involvement extends to organizing national and international conferences 🎤📅 and acting as a program committee member for IJCAI, AAAI, CVPR, and others 🤝📊, reflecting his strong engagement with the global AI and computing community.

🔍 Research Focus Category 

Prof. Liu’s research lies at the intersection of artificial intelligence 🤖, machine learning 📚, and data science 📊. He specializes in hash learning, outlier detection, feature selection, and multimedia systems 🎥. His focus extends to practical applications in big data analytics 🗃️ and intelligent systems 💡. With a keen interest in mining patterns from complex datasets, his work contributes significantly to pattern recognition 🧠 and cybernetics 🛡️. He aims to bridge theory and real-world implementation through intelligent algorithms that enhance automated decision-making systems 🧮. His interdisciplinary approach empowers robust AI models with scalable and sustainable solutions 🌍.

🏆 Awards & Honors 

  • 📈 H-index of 17 according to Google Scholar 🧠

  • 📝 Over 50 publications in leading journals such as IEEE TKDE, TNNLS, TMM, TSMC, and more 📚

  • 🧑‍💼 Editor-in-Chief, Int. J. of Intelligence and Sustainable Computing

  • 🛠️ Associate Editor, Int. J. of Artificial Intelligence and Tools

  • ➗ Editor, Mathematics

  • 🧮 Lead Guest Editor for Neural Computing and Applications (NCAA)

  • 💻 Lead Guest Editor for Computing and Informatics (CAI)

  • 🎤 Organising Chair for 2015 National Conf. of Theoretical Computer Science

  • 📊 Organising Chair for 2014 China Conference on Data Mining

  • 🎓 Program Committee Member for top AI conferences: IJCAI, AAAI, CVPR, ADMA, ICBK, KSEM

Publication Top Notes

🔍 1. Outlier Detection Using Local Density and Global Structure

  • Authors: H. Liu, Huawen; S. Zhang, Shichao; Z. Wu, Zongda; X. Li, Xuelong

  • Journal: Pattern Recognition, 2025

  • Citations: 7

  • Summary: This article proposes a novel outlier detection method combining local density estimation with global structural features. It’s likely useful for anomaly detection in high-dimensional or graph-structured data.

🧠 2. Select Your Own Counterparts: Self-Supervised Graph Contrastive Learning With Positive Sampling

  • Authors: Z. Wang, Zehong; D. Yu, Donghua; S. Shen, Shigen; S. Yao, Shuang; M. Guo, Maozu

  • Journal: IEEE Transactions on Neural Networks and Learning Systems, 2025

  • Citations: 2

  • Summary: Focuses on self-supervised learning with graph contrastive methods, improving representation learning by selecting reliable positive samples for contrastive training.

🗣️ 3. Amharic Spoken Digits Recognition Using Convolutional Neural Network

  • Authors: T.A. Ayall, Tewodros Alemu; C. Zhou, Chuangjun; H. Liu, Huawen; S.T. Abate, Solomon Teferra; M. Adjeisah, Michael

  • Journal: Journal of Big Data, 2024 (Open Access)

  • Citations: 3

  • Summary: Presents a CNN-based model for recognizing spoken digits in Amharic, an under-resourced African language — showcasing multilingual AI applications.

🧠 4. An Improved Deep Hashing Model for Image Retrieval With Binary Code Similarities

  • Authors: H. Liu, Huawen; Z. Wu, Zongda; M. Yin, Minghao; X. Zhu, Xinzhong; J. Lou, Jungang

  • Access: Open Access

  • Citations: 0

  • Summary: Describes a deep hashing method that optimizes binary similarity in hash code space for more effective image retrieval.

🧠 5. LGAD: Local and Global Attention Distillation for Efficient Semantic Segmentation

  • Authors: C. Wang, Chen; Y. Qi, Yafei; Q. Li, Qi; H. Liu, Huawen

  • Type: Conference Paper (Open Access)

  • Citations: 1

  • Summary: Proposes an attention distillation method combining local and global context for lightweight semantic segmentation, improving performance while keeping models efficient.

Conclusion:

Dr. Huawen Liu’s exceptional research contributions, leadership in academic organizations, and active engagement in the scientific community make him a strong candidate for the Distinguished Scientist Award. His sustained impact on the field of machine learning and AI, along with his contributions to both theoretical and applied research, exemplify the qualities deserving of such an esteemed recognition.

Mehmet Yilmaz | Artificial Neural Networks | Best Researcher Award

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Mr. Mehmet Yilmaz | Artificial Neural Networks | Best Researcher Award

Mr, Mehmet Yilmaz, Kayseri University, Turkey

Mehmet Yilmaz is a lecturer in the Department of Architecture and Urban Planning at Kayseri University, Turkey. With an academic background in Geomatic Engineering from Erciyes University, he brings expertise in geotechnical engineering, real estate valuation, and geographic information systems (GIS) to his role. Currently pursuing his doctorate, Mr. Yilmaz’s teaching and research contributions focus on engineering applications in urban environments, including courses on land measurement, urban information systems, and property law. His work is dedicated to exploring innovative solutions in GIS and urban planning, addressing practical challenges in real estate valuation and geotechnical engineering.

PROFILE

Orcid Profile

Educational Details

Mr. Mehmet Yilmaz is a faculty member at Kayseri University, Turkey, where he specializes in engineering and urban planning. He is currently pursuing his Doctorate in Geomatic Engineering at Erciyes University’s Institute of Science (Fen Bilimleri Enstitüsü), continuing his journey in the same field in which he obtained both his postgraduate degree (2019-2021) and undergraduate degree (2007-2012). This solid academic foundation has equipped him with specialized skills in geographic information systems, geotechnical engineering, and real estate valuation.

Professional Experience

Since 2018, Mr. Yilmaz has served as a lecturer at Kayseri University in the Tomarza Mustafa Akıncıoğlu Vocational School of Architecture and Urban Planning. He previously taught at Erciyes University in the same department (2017-2018). Throughout his career, he has taught a wide array of courses, including Land Measurement, Expropriation Techniques, Real Estate Law, Urban Information Systems, and Real Estate Valuation Techniques, as well as foundational courses such as Mathematics and Basic Law. His commitment to teaching and hands-on field knowledge has contributed to his expertise in applied engineering and planning education.

Research Interests

Mr. Yilmaz’s research interests span several critical areas within engineering and urban planning, including geotechnical engineering, real estate valuation, geographic information systems (GIS), and image processing. His research has previously focused on topics such as property tax loss in mass valuation, as exemplified by his postgraduate thesis, which investigated the impacts of mass valuation on tax losses in the Kayseri region. This study highlights his interest in the integration of GIS and valuation techniques to address real-world urban planning challenges.

Top Notable Publications

Mehmet Yilmaz (2024)
Title: Hiperspektral görüntülerde Relief-F algoritması ile band seçimi
Source: Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
Publication Date: 2024-04-02
DOI: 10.28948/ngumuh.1408200

Mehmet Yilmaz (2023)
Title: Investigation of Real Estate Tax Leakage Loss Rates with ANNs
Source: Buildings
Publication Date: 2023-09-28
DOI: 10.3390/buildings13102464
ISSN: 2075-5309

Mehmet Yilmaz (2021)
Title: Determination of Housing Prices with Mass Appraisal in Turkey
Source: Ankara V. International Scientific Research Congress
Publication Date: 2021-10-18
(Conference abstract, no DOI provided)

Conclusion

Mr. Mehmet Yilmaz’s academic background, teaching experience, research interests, certifications, and publication record collectively establish him as a dedicated researcher in the fields of geomatics, urban planning, and real estate valuation. His interdisciplinary approach, integrating advanced technologies like GIS, hyperspectral imaging, and neural networks, is noteworthy for solving real-world challenges in property valuation and urban information systems. Given these qualifications, Mr. Yilmaz is a strong candidate for the Research for Best Researcher Award, with demonstrated potential for further contributions to his field.