Guiyang University, at School of Computer Science, China.
Assoc. Prof. Dr. Linchang Zhao is an accomplished academic and researcher at Guiyang University in China, specializing in machine learning, deep learning, few-shot learning, and optimization algorithms. He holds a Ph.D. in Computer Science from Chongqing University, with additional degrees in Mathematics and Statistics, and Computer Science. Dr. Zhao’s research focuses on data mining, imbalanced learning, and software defect prediction, where he has made significant contributions through innovative techniques like cost-sensitive meta-learning classifiers and deep neural networks. His work has been widely published in prominent journals such as IEEE Access and Neurocomputing, and he holds patents related to small sample data learning and imbalanced data prediction. With experience as a graduate tutor and mentor, Dr. Zhao continues to shape the next generation of researchers while actively contributing to high-impact projects funded by the National Natural Science Foundation of China.
Professional Profile
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Education 🎓
Dr. Linchang Zhao completed his Ph.D. in Computer Science from Chongqing University (2017–2021), where he focused on advancements in deep learning and machine learning. He also holds a Master of Engineering in Mathematics and Statistics from Qiannan Normal College for Nationalities (2015–2017) and a Bachelor of Science in Computer Science from Northeast Petroleum University (2009–2013).
Experience đź’Ľ
Dr. Zhao currently serves as an Associate Professor and graduate tutor at Guiyang University, where he mentors students and leads research initiatives. His academic career is highlighted by his active participation in several high-impact projects, including those funded by the National Natural Science Foundation of China. His work on machine learning, especially in software defect prediction and optimization, has garnered attention in both academic and industrial circles.
Research Interest 🔬
Dr. Zhao’s research primarily revolves around machine learning, few-shot learning, deep learning, optimization algorithms, and meta-learning. He is particularly interested in data mining, imbalanced learning, and software defect prediction, using cutting-edge techniques such as cost-sensitive meta-learning classifiers and deep neural networks. His work aims to address challenges in real-world applications, particularly in small datasets and imbalanced data contexts.
Award 🏅
Throughout his career, Dr. Zhao has made substantial contributions to his field, earning recognition for his innovative research. He has been awarded various honors for his work on software defect prediction and cost-sensitive machine learning methods. His contributions to machine learning in the context of small sample data and imbalanced datasets have been highly praised.
Top Noted Publication đź“‘
Design and Implementation of GPU Pass-Through System Based on OpenStack Computation
Authors: Linchang Zhao, Yu Jin, Guoqing Hu, Wenxi Zhou, Hao Wei, Ruiping Li, Xu Zhu, Yongchi Xu, Jiulin Jin, Qianbo Li
Journal: Computation
DOI: 10.3390/computation13020038
Year of Publication: 2025
RFAConv-CBM-ViT: Enhanced Vision Transformer for Metal Surface Defect Detection
Authors: Hao Wei, Linchang Zhao, Ruiping Li, Mu Zhang
Journal: The Journal of Supercomputing
DOI: 10.1007/s11227-024-06662-0
Year of Publication: 2025
Siamese Dense Neural Network for Software Defect Prediction With Small Data
Authors: Linchang Zhao, Zhaowei Shang, Ling Zhao, Anyong Qin, Yuan Yan Tang
Journal: IEEE Access
DOI: 10.1109/ACCESS.2018.2889061
Year of Publication: 2019
A Cost-Sensitive Meta-Learning Classifier: SPFCNN-Miner
Authors: Linchang Zhao
Journal: Future Generation Computer Systems
DOI: 10.1016/j.future.2019.05.080
Year of Publication: 2019
Software Defect Prediction via Cost-Sensitive Siamese Parallel Fully-Connected Neural Networks
Authors: Linchang Zhao, Zhaowei Shang, Ling Zhao, Taiping Zhang, Yuan Yan Tang
Journal: Neurocomputing
DOI: 10.1016/j.neucom.2019.03.076
Year of Publication: 2019
Conclusion
Linchang Zhao’s combination of advanced research, practical innovations, and contributions to education makes him a strong candidate for the Best Researcher Award. His ability to address real-world problems through machine learning and his efforts to foster academic growth through mentorship positions him as a leader in his field. To further solidify his position as a top researcher, increased interdisciplinary collaborations and global visibility would be beneficial.