Zhonghua Liu | Pattern Recognition | Best Researcher Award

Dr. Zhonghua Liu | Pattern Recognition | Best Researcher Award

Prof., Zhejiang Ocean University, China

Dr. Zhonghua Liu is a distinguished professor and researcher in the field of Pattern Recognition, Image Processing, and Machine Learning 🤖📸. With an extensive academic background and rich research experience, he has significantly contributed to transfer learning, subspace learning, and sparse representation. Currently, he serves as a Professor at Zhejiang Ocean University, China 🇨🇳, and has previously held key positions at Henan University of Science and Technology. His research excellence is reflected in numerous high-impact publications, patents, and funded projects, earning him prestigious academic honors.

Publication Profile

🎓 Education

Dr. Zhonghua Liu pursued his Ph.D. in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology 🎓 in 2011, under the guidance of Prof. Zhong Jin. He also holds an M.S. degree in Computer Software and Theory from Xihua University (2005), mentored by Prof. Xinwei Liu. His strong educational foundation has shaped his expertise in artificial intelligence and computational learning.

💼 Experience

Dr. Liu has a diverse and dynamic academic career spanning over two decades. Since July 2023, he has been a Professor at Zhejiang Ocean University 🏛️. Before this, he was a Professor at Henan University of Science and Technology (2021–2023) and an Associate Professor at the same university from 2005 to 2020. His international exposure includes working as a Visiting Scholar at the University of Technology Sydney (2015–2016) 🌏. His research collaborations extend to Xidian University and China Airborne Missile Academy, where he contributed significantly to machine learning and image recognition advancements.

🏆 Awards and Honors

Dr. Liu has received numerous awards for his contributions to academia and research. He was recognized as an Excellent Teacher at Henan University of Science and Technology (2012) 🍎 and received the Second Award for Teaching Quality in 2013. In recognition of his research, he was honored as an Outstanding Postdoctoral Researcher in Henan Province (2013) and was designated Luoyang Youth Academic and Technical Leader in 2014 🏅.

🔬 Research Focus

Dr. Liu specializes in Pattern Recognition, Image Processing, and Machine Learning 📊. His work revolves around transfer learning, domain adaptation, sparse representation, and dimensionality reduction. His research aims to enhance artificial intelligence techniques for image analysis and classification, bridging the gap between theoretical advancements and real-world applications.

🔖 Conclusion

Dr. Zhonghua Liu is a leading researcher and educator in the field of machine learning and pattern recognition. His extensive academic career, research contributions, and funded projects underscore his expertise in subspace learning, sparse representation, and image processing. With a strong international presence, impactful publications, and numerous awards, Dr. Liu continues to shape the landscape of artificial intelligence and computational intelligence research 🏆

📚 Publications

Discriminative transfer regression for low-rank and sparse subspace learning

Engineering Applications of Artificial Intelligence, 2024

DOI: 10.1016/j.engappai.2024.108445

Domain adaptive learning based on equilibrium distribution and dynamic subspace approximation

Expert Systems with Applications, 2024, Vol. 249

DOI: 10.1016/j.eswa.2024.123673

Robust manifold discriminative distribution adaptation for transfer subspace learning

Expert Systems with Applications, 2023, Vol. 238

DOI: 10.1016/j.eswa.2023.122117

Manifold transfer subspace learning based on double relaxed discriminative regression

Artificial Intelligence Review, 2023, Vol. 56(1), pp. 959-981

DOI: 10.1007/s10462-023-10404-7

Discriminative sparse least square regression for semi-supervised learning

Information Sciences, 2023, Vol. 636

DOI: 10.1016/j.ins.2023.118903

Dynamic classifier approximation for unsupervised domain adaptation

Signal Processing, 2023, Vol. 206

DOI: 10.1016/j.sigpro.2023.108915

Robust sparse low-rank embedding for image dimension reduction

Applied Soft Computing, 2021, Vol. 113

DOI: 10.1016/j.asoc.2021.20211129

Structured optimal graph-based sparse feature extraction for semi-supervised learning

Signal Processing, 2020, Vol. 170

DOI: 10.1016/j.sigpro.2020.107456

Discriminative low-rank preserving projection for dimensionality reduction

Applied Soft Computing, 2019, Vol. 85

DOI: 10.1016/j.asoc.2019.105908

Nonnegative low-rank representation-based manifold embedding for semi-supervised learning

Knowledge-Based Systems, 2017, Vol. 136

DOI: 10.1016/j.knosys.2017.07.019