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.