Qizhe Yang
Lecturer — College of Information, Mechanical and Electrical Engineering, SHNU
My research spans three directions. In formal verification, I study Vector Addition Systems with States (VASS) — focusing on reachability problems, complexity characterizations, and recent results on geometrically low-dimensional VASS. In computer vision, I work on image restoration and enhancement (rain removal, haze removal, low-light enhancement), including the development of MCD-Net and related deep architectures. In trustworthy AI, I investigate interpretability of deep neural networks and explore AI-assisted approaches to theoretical computer science research.
Prospective Students: I am currently recruiting 2 master's students. I am looking for candidates with strong mathematical backgrounds who are interested in formal verification, theoretical computer science, computer vision, or trustworthy AI. If you are motivated and curious, please feel free to reach out by email.
Research Directions
Infinite-State Systems & VASS
Reachability and complexity of Vector Addition Systems with States (VASS); low-dimensional VASS; formal verification of infinite-state models.
Computer Vision
Image restoration, including rain removal, haze removal, and low-light enhancement. Developing deep architectures such as MCD-Net.
Trustworthy AI
Interpretability of deep learning models; AI-assisted theoretical research; principled approaches to reliable and explainable AI systems.