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 ๐Ÿ‘จโ€๐Ÿซ, a distinguished academic at Shaoxing University ๐Ÿ‡จ๐Ÿ‡ณ since 2010, holds a Ph.D. and Ms.D. in Computer Science from Jilin University ๐Ÿง ๐Ÿ’ป. He expanded his research globally as a postdoc at the University of South Australia ๐Ÿ‡ฆ๐Ÿ‡บ (2012โ€“2013) and a visiting fellow at the University of Texas at San Antonio ๐Ÿ‡บ๐Ÿ‡ธ (2018โ€“2019). His work spans hash learning, AI, big data, and machine learning ๐Ÿค–๐Ÿ“Š. With over 50 publications ๐Ÿ“š in top-tier journals, he actively contributes as an editor and conference organizer. He holds an H-index of 17 ๐Ÿ“ˆ and continues to shape intelligent computing worldwide ๐ŸŒ.

Professional Profile:

Google Scholar

Scopus

๐ŸŽ“ Education & Experienceย 

  • ๐ŸŽ“ Ph.D. & Ms.D. in Computer Science from Jilin University, China ๐Ÿ‡จ๐Ÿ‡ณ (Top-10 university)

  • ๐Ÿง‘โ€๐Ÿ”ฌ Postdoctoral Researcher at University of South Australia ๐Ÿ‡ฆ๐Ÿ‡บ (2012โ€“2013)

  • ๐ŸŒ Visiting Fellow at University of Texas at San Antonio, USA ๐Ÿ‡บ๐Ÿ‡ธ (2018โ€“2019)

  • ๐Ÿ‘จโ€๐Ÿซ Professor at Shaoxing University since July 2010 ๐Ÿซ

  • ๐Ÿ“ Over 50 peer-reviewed publications in high-impact journals and conferences ๐Ÿ“š

๐ŸŒฑ Professional Developmentย 

Prof. Liu has actively participated in shaping the research community ๐ŸŒ. He serves as the Editor-in-Chief (EIC) of the International Journal of Intelligence and Sustainable Computing ๐Ÿง ๐Ÿ’ก, Associate Editor for International Journal of Artificial Intelligence and Tools ๐Ÿ› ๏ธ, and Editor for Mathematics โž—๐Ÿ“˜. He has also led special issues as Guest Editor in Neural Computing and Applications ๐Ÿงฎ and Computing and Informatics ๐Ÿ’ป. His involvement extends to organizing national and international conferences ๐ŸŽค๐Ÿ“… and acting as a program committee member for IJCAI, AAAI, CVPR, and others ๐Ÿค๐Ÿ“Š, reflecting his strong engagement with the global AI and computing community.

๐Ÿ” Research Focus Categoryย 

Prof. Liuโ€™s research lies at the intersection of artificial intelligence ๐Ÿค–, machine learning ๐Ÿ“š, and data science ๐Ÿ“Š. He specializes in hash learning, outlier detection, feature selection, and multimedia systems ๐ŸŽฅ. His focus extends to practical applications in big data analytics ๐Ÿ—ƒ๏ธ and intelligent systems ๐Ÿ’ก. With a keen interest in mining patterns from complex datasets, his work contributes significantly to pattern recognition ๐Ÿง  and cybernetics ๐Ÿ›ก๏ธ. He aims to bridge theory and real-world implementation through intelligent algorithms that enhance automated decision-making systems ๐Ÿงฎ. His interdisciplinary approach empowers robust AI models with scalable and sustainable solutions ๐ŸŒ.

๐Ÿ† Awards & Honorsย 

  • ๐Ÿ“ˆ H-index of 17 according to Google Scholar ๐Ÿง 

  • ๐Ÿ“ Over 50 publications in leading journals such as IEEE TKDE, TNNLS, TMM, TSMC, and more ๐Ÿ“š

  • ๐Ÿง‘โ€๐Ÿ’ผ Editor-in-Chief, Int. J. of Intelligence and Sustainable Computing

  • ๐Ÿ› ๏ธ Associate Editor, Int. J. of Artificial Intelligence and Tools

  • โž— Editor, Mathematics

  • ๐Ÿงฎ Lead Guest Editor for Neural Computing and Applications (NCAA)

  • ๐Ÿ’ป Lead Guest Editor for Computing and Informatics (CAI)

  • ๐ŸŽค Organising Chair for 2015 National Conf. of Theoretical Computer Science

  • ๐Ÿ“Š Organising Chair for 2014 China Conference on Data Mining

  • ๐ŸŽ“ Program Committee Member for top AI conferences: IJCAI, AAAI, CVPR, ADMA, ICBK, KSEM

Publication Top Notes

๐Ÿ” 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.

๐Ÿง  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.

๐Ÿ—ฃ๏ธ 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 โ€” showcasing multilingual AI applications.

๐Ÿง  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.

๐Ÿง  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.

Ali Anaissi | Data Science | Best Researcher Award

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Dr. Ali Anaissi | Data Science | Best Researcher Award

Senior Lecturer at University of Technology Sydney, Australia

Dr. Ali Anaissi ๐ŸŽ“ is a seasoned data scientist and academic, currently a Senior Lecturer at the University of Technology Sydney ๐Ÿ‡ฆ๐Ÿ‡บ and Deputy Director of the Master of Data Science and Innovation. With a Ph.D. in Data Science from UTS ๐Ÿง  and a background in Computer & Communication Engineering, he brings a rich blend of academic and industry experience. He has worked at prestigious institutions including the University of Sydney and CSIRO-Data61 ๐Ÿ”ฌ. His research spans machine learning, anomaly detection, and structural health monitoring, contributing significantly to data-driven innovations ๐Ÿค–๐Ÿ“Š.

Professional Profile:

Orcid

Scopus

Google Scholar

Education & Experience๐ŸŽ“

  • ๐ŸŽ“ Ph.D. in Data Science, University of Technology Sydney (2009โ€“2013)

  • ๐ŸŽ“ Bachelorโ€™s in Computer & Communication Engineering, Islamic University of Lebanon (2000โ€“2005)

  • ๐Ÿ‘จโ€๐Ÿซ Senior Lecturer, UTS (2022โ€“Present)

  • ๐Ÿ‘จโ€๐Ÿซ Lecturer, University of Sydney (2017โ€“2022)

  • ๐Ÿงช Research Scientist, CSIROโ€“Data61 (2015โ€“2017)

  • ๐Ÿ‘จโ€๐Ÿ’ป Software Developer, Agile Technologies, Lebanon (2004โ€“2008)

  • ๐Ÿง‘โ€๐Ÿซ Research Assistant, Tutor & Lecturer, UTS (2010โ€“2016)

Professional Development๐Ÿ“š

Dr. Anaissi continually enhances his expertise through active participation in professional and academic development activities ๐Ÿ“ˆ. He engages in international conferences, publishes high-impact research articles ๐Ÿ“‘, and collaborates with industry partners on real-world data science challenges ๐ŸŒ. His role as a mentor to Ph.D. candidates fosters growth in the next generation of researchers ๐Ÿ‘จโ€๐ŸŽ“๐Ÿ‘ฉโ€๐ŸŽ“. He is also involved in curriculum development and education innovation at UTS and the University of Sydney ๐Ÿ“˜๐Ÿ’ก. These efforts reflect his commitment to bridging theory and practice, ensuring his work remains at the forefront of data science advancement ๐Ÿš€๐Ÿ”.

Research Focus๐Ÿง 

Dr. Anaissiโ€™s research lies at the intersection of data science, machine learning, and real-world application domains ๐Ÿค–๐Ÿ“Š. His core focus includes anomaly detection, structural health monitoring, cyber security analytics ๐Ÿ”, and predictive modeling. By developing intelligent algorithms and data-driven models, he improves the accuracy, safety, and efficiency of engineering and cyber systems โš™๏ธ๐Ÿ›ฐ๏ธ. He often works on interdisciplinary teams, blending data science with civil engineering, health informatics, and IT infrastructure. His innovations contribute to smarter decision-making systems, with a strong emphasis on explainability and robust performance across diverse environments ๐ŸŒ๐Ÿงช.

Awards & Honors๐Ÿ…

  • ๐Ÿ† Honors Distinction (2005) โ€“ Awarded for outstanding dissertation on Face Detection and Recognition ๐Ÿ“ธ๐Ÿง 

  • ๐Ÿฅ‡ Academic Achievement Award โ€“ Recognized for excellence in academic performance during undergraduate studies ๐ŸŽ“๐Ÿ“˜

  • ๐ŸŒŸ Best Paper Presentation (various conferences) โ€“ For impactful research presentations in data analytics and AI forums ๐ŸŽค๐Ÿ“Š

Publication Top Notes

1. A Balanced Iterative Random Forest for Gene Selection from Microarray Data

Authors: A. Anaissi, P.J. Kennedy, M. Goyal, D.R. Catchpoole
Published in: BMC Bioinformatics, 2013, Volume 14, Article 1, Pages 1โ€“10
Citations: 93
DOI: 10.1186/1471-2105-14-261
Summary:
This study presents a Balanced Iterative Random Forest (BIRF) algorithm for gene selection from high-dimensional microarray datasets. The method iteratively refines feature subsets to enhance classification performance while addressing class imbalance. It was evaluated on multiple cancer datasets and showed improvements in both gene selection stability and classification accuracy over existing methods.

2. Ensemble Feature Learning of Genomic Data Using Support Vector Machine

Authors: A. Anaissi, M. Goyal, D.R. Catchpoole, A. Braytee, P.J. Kennedy
Published in: PLOS ONE, 2016, Volume 11, Issue 6, e0157330
Citations: 52
DOI: 10.1371/journal.pone.0157330
Summary:
The authors propose an ensemble feature learning approach that integrates SVMs and gene ranking techniques to extract key genomic features from cancer data. The approach is validated using pediatric acute lymphoblastic leukemia (ALL) data, enhancing classification accuracy and interpretability for biomarker discovery.

3. Smart Pothole Detection System Using Vehicle-Mounted Sensors and Machine Learning

Authors: A. Anaissi, N.L.D. Khoa, T. Rakotoarivelo, M.M. Alamdari, Y. Wang
Published in: Journal of Civil Structural Health Monitoring, 2019, Volume 9, Pages 91โ€“102
Citations: 45
DOI: 10.1007/s13349-019-00313-5
Summary:
This work introduces a machine learning-based system for real-time pothole detection using vehicle-mounted accelerometers and gyroscopes. A supervised learning model is trained to classify road conditions, providing a low-cost, scalable solution for road infrastructure monitoring.

4. A Tensor-Based Structural Damage Identification and Severity Assessment

Authors: A. Anaissi, M. Makki Alamdari, T. Rakotoarivelo, N.L.D. Khoa
Published in: Sensors, 2018, Volume 18, Issue 1, Article 111
Citations: 42
DOI: 10.3390/s18010111
Summary:
The authors propose a tensor decomposition-based method to assess structural damage using vibration data. The approach not only detects damage but also estimates severity, offering a robust solution for structural health monitoring (SHM) systems.

5. hsa-miR-29c and hsa-miR-135b Differential Expression as Potential Biomarker of Gastric Carcinogenesis

Authors: A.F. Vidal, A.M.P. Cruz, L. Magalhรฃes, A.L. Pereira, A.K.M. Anaissi, N.C.F. Alves, et al.
Published in: World Journal of Gastroenterology, 2016, Volume 22, Issue 6, Pages 2060โ€“2070
Citations: 42
DOI: 10.3748/wjg.v22.i6.2060
Summary:
This biomedical study investigates the differential expression of microRNAs hsa-miR-29c and hsa-miR-135b as biomarkers for gastric cancer. The research combines molecular profiling with clinical data, suggesting these miRNAs as promising early diagnostic markers for gastric carcinogenesis.

Conclusion

Dr. Ali Anaissi embodies the ideal candidate for a Best Researcher Award. His multi-dimensional research profile, impactful publications, dedicated supervision, and leadership roles make him a standout in the field of data science and applied AI.

He not only advances scientific knowledge but also translates it into real-world solutions, addressing global challenges in smart cities, healthcare, cybersecurity, and infrastructure resilience.