Assoc. Prof. Dr. Linchang Zhao, School of Computer Science, China
Dr. Linchang Zhao is an Associate Professor and graduate tutor at Guiyang University, China, specializing in machine learning, deep learning, few-shot learning, optimization algorithms, and meta-learning. He earned his Ph.D. in Computer Science from Chongqing University and holds an M.E. in Mathematics and Statistics from Qiannan Normal College for Nationalities, as well as a B.S. in Computer Science from Northeast Petroleum University. His research focuses on data mining, imbalanced learning, and developing novel learning models for small data scenarios. Dr. Zhao has authored numerous high-impact publications in top-tier journals and conferences, particularly in cost-sensitive meta-learning and software defect prediction. He has also contributed to several national and provincial research projects and holds patents related to small sample learning and software defect prediction. His expertise in artificial intelligence and deep learning continues to drive innovation in intelligent computing and data-driven decision-making.
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Summary of Suitability for the Best Researcher Award
Linchang Zhao is a highly deserving candidate for the Best Researcher Award, given his significant contributions to machine learning, deep learning, few-shot learning, and optimization algorithms. His research has pushed the boundaries of artificial intelligence (AI) and computational intelligence, particularly in the areas of data mining, imbalanced learning, and meta-learning.
Education 🎓
- Ph.D. in Computer Science – Chongqing University, China (2017.9 – 2021.7)
- M.E. in Mathematics and Statistics – Qiannan Normal College for Nationalities, China (2015.9 – 2017.7)
- B.S. in Computer Science – Northeast Petroleum University, China (2009.9 – 2013.7)
Work Experience 🏫
- Associate Professor & Graduate Tutor – Guiyang University, China (Present)
Achievements 🏆
- Published numerous research papers in prestigious journals such as IEEE Transactions on Cybernetics, Future Generation Computer Systems, and Neurocomputing.
- Contributed to multiple national and international research projects on deep learning, machine learning, and data mining.
- Developed advanced models for software defect prediction, cost-sensitive learning, and imbalanced data classification.
Awards & Honors 🎖️
- Patent Holder for innovative methods in software defect prediction and small-sample learning classifiers.
- Principal Investigator of a National Natural Science Foundation of China project on Deep Learning (2018–2020).
- Contributor to significant projects in military intelligence, education big data, and oil exploration risk assessment.
- Recognized Researcher in Few-Shot Learning, Meta-Learning, and Optimization Algorithms.
Publication Top Notes:
Design and Implementation of GPU Pass-Through System Based on OpenStack
RFAConv-CBM-ViT: enhanced vision transformer for metal surface defect detection
Siamese Dense Neural Network for Software Defect Prediction With Small Data
A cost-sensitive meta-learning classifier: SPFCNN-Miner
Software defect prediction via cost-sensitive Siamese parallel fully-connected neural networks