Rate My Professor Ji Wang

Ji Wang's research centers on formal methods and software engineering, particularly symbolic execution techniques for software verification, testing, and reliability analysis. His notable contributions include the paper 'Kullback-Leibler Divergence-Based Out-of-Distribution Detection for Autonomous Driving' published in IEEE Transactions on Knowledge and Data Engineering in 2024, 'Optimal Stability of KL Divergence under Gaussian Perturbations' co-authored and available on arXiv in 2026, 'Efficient Multiplex Symbolic Execution with Adaptive Search Strategy' presented at the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE 2020), 'Feature selection in machine learning: A new perspective' in Neurocomputing (2018), and 'Demand-Driven Memory Leak Detection Based on Flow- and Context-Sensitive Pointer Analysis' in 2009. These publications have significantly influenced advancements in automated software analysis, out-of-distribution detection for AI systems, and dependable computing practices. Through his scholarly work and mentorship, Ji Wang continues to drive innovation in high-assurance software development at Hunan University.