Zicong Chen at Jinan University, China📖
Zicong Chen is a graduate student pursuing a Master’s in Computer Application Technology at Jinan University, China. He holds a Bachelor’s in Computer Science and Technology from Shantou University, with an exchange program at Hangzhou Dianzi University. Zicong’s research focuses on explainable artificial intelligence, particularly in adversarial attacks on deep learning models, their robustness, and their application in medical imaging and industrial automation. He has contributed to several high-impact papers and has gained a strong foundation in various programming languages, web development, and database management.
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Education Background🎓
- M.Sc. in Computer Application Technology (2022.06 – 2025.06), Jinan University, China (Full-time graduate program)
- B.Sc. in Computer Science and Technology (2018.06 – 2022.09), Shantou University, China (Full-time undergraduate program)
- B.Sc. in Computer Science and Technology (2020.06 – 2020.09), Hangzhou Dianzi University, China (Undergraduate exchange program)
Professional Experience🌱
Zicong Chen has engaged in various academic research projects during his studies, focusing on artificial intelligence, deep learning, and its applications in fields like medical imaging and industrial automation. He has contributed as the first author, co-first author, or corresponding author in several high-impact research papers published in journals such as Engineering Applications of Artificial Intelligence, IEEE Transactions on Medical Imaging, and Pattern Recognition. His professional experience also includes practical applications of programming in various languages such as Python, C++, Java, and Go, with additional expertise in web development, databases, and containerization tools like Docker.
Zicong’s research interests include:
- Explainability and robustness of adversarial trained convolutional neural networks (CNNs)
- Counterfactual generation for medical image classification and lesion localization
- Neural network optimization using Markov chain approaches
- Statistical physics interpretation of CNN vulnerabilities and classification reliability
- Graph-based adversarial robustness evaluation in industrial automation systems
Author Metrics
Publications Top Notes 📄
1. Score-Based Counterfactual Generation for Interpretable Medical Image Classification and Lesion Localization
- Authors: K. Wang, Z. Chen, M. Zhu, J. Weng, T. Gu
- Journal: IEEE Transactions on Medical Imaging
- Year: 2024
- Volume: 43
- Issue: 10
- Pages: 3596–3607
- DOI: 10.1109/TMI.2024.3375357
- Citations: 4
2. A Statistical Physics Perspective: Understanding the Causality Behind Convolutional Neural Network Adversarial Vulnerability
- Authors: K. Wang, M. Zhu, Z. Chen, W. Ding, T. Gu
- Journal: IEEE Transactions on Neural Networks and Learning Systems
- Year: 2024
- DOI: 10.1109/TNNLS.2024.3359269
- Citations: 0
3. Uncovering Hidden Vulnerabilities in Convolutional Neural Networks through Graph-Based Adversarial Robustness Evaluation
- Authors: K. Wang, Z. Chen, X. Dang, S.-M. Yiu, J. Weng
- Journal: Pattern Recognition
- Year: 2023
- Volume: 143
- Article Number: 109745
- DOI: 10.1016/j.patcog.2023.109745
- Citations: 15
4. Statistics-Physics-Based Interpretation of the Classification Reliability of Convolutional Neural Networks in Industrial Automation Domain
- Authors: K. Wang, Z. Chen, M. Zhu, S. Izzo, G. Fortino
- Journal: IEEE Transactions on Industrial Informatics
- Year: 2023
- Volume: 19
- Issue: 2
- Pages: 2165–2172
- DOI: 10.1109/TII.2022.3202950
- Citations: 8
5. Enterovirus 71 Non-Structural Protein 3A Hijacks Vacuolar Protein Sorting 25 to Boost Exosome Biogenesis to Facilitate Viral Replication
- Authors: Z. Ruan, Y. Liang, Z. Chen, J. Wu, Z. Luo
- Journal: Frontiers in Microbiology
- Year: 2022
- Volume: 13
- Article Number: 1024899
- DOI: 10.3389/fmicb.2022.1024899
- Citations: 10
Conclusion
Zicong Chen is undoubtedly a strong candidate for the Best Researcher Award due to his innovative research, interdisciplinary expertise, and contributions to the field of artificial intelligence and machine learning. His work on adversarial robustness, explainable AI, and their applications to medical imaging and industrial automation has significant potential to drive future advancements in these areas. While he has made remarkable progress, expanding the impact of his research on real-world applications and further increasing his engagement with industry could elevate his work to even greater heights. His continued leadership in collaborative research and commitment to advancing AI will undoubtedly make him a key figure in his field. Therefore, he is a highly deserving candidate for the Best Researcher Award.