Rate My Professor Wei Zhong

Professor Wei Zhong serves as Professor and Assistant Chair for Computer Science in the Division of Mathematics and Computer Science at the University of South Carolina Upstate in Spartanburg. He earned his Ph.D. in 2006 from Georgia State University. Zhong is actively involved in university governance as a member of the Faculty Senate, representing the Math and Computer Science division, and contributes to the editorial board of Scientific Research Publishing. His academic career at USC Upstate includes progression from Assistant Professor, as noted in earlier university catalogs, to his current professorial role. Zhong's teaching portfolio features computer science and artificial intelligence courses, where he incorporates practical, hands-on laboratories on technologies such as facial recognition, object detection for alert systems, and projects utilizing the ChatGPT API for integrating AI into applications like automated food ordering services. He stresses the importance of understanding AI's limitations, describing it as pattern-based memorization rather than true reasoning, and encourages students to critically analyze AI outputs for optimization and the underlying rationale.

Zhong's research specializations include machine learning, bioinformatics, computational biology, protein structure prediction, clustering algorithms, and cybersecurity applications of deep learning. Key publications encompass 'Applying Big Data Based Deep Learning System to Intrusion Detection' in Big Data Mining and Analytics (2020), co-authored with Ning Yu and Chunyu Ai; 'Multi-level Clustering Support Vector Machine Trees for Improved Protein Local Structure Prediction' with Jieyue He, Xiujuan Chen, and Yi Pan; 'Clustering Support Vector Machines for Local Protein Structure Prediction' in Expert Systems with Applications (2007); 'Predicting Local Protein 3D Structures Using Clustering Deep Recurrent Neural Network' (2022); 'A Dual-Approach Framework for Enhancing Network Traffic Anomaly Detection' (2024); and 'An Improved Method for Completely Uncertain Biological Networks' (2015). His work supports student research initiatives, including presentations on generating new protein samples for structure prediction accuracy and new malware samples using tree-based methods at the South Carolina Upstate Research Symposium. Zhong's contributions extend to conference proceedings and collaborations on topics like partner-matching frameworks for social activity communities and hybrid resampling algorithms for particle filters.