Rate My Professor Xingyu Zhao

Zhao's research specializes in safety assurance for learning-enabled systems, particularly autonomous robots and vehicles. His academic interests encompass probabilistic model checking, Bayesian inference for verification, robustness evaluation of deep learning models, and runtime monitoring for AI safety. Key publications include 'Bayesian learning for the robust verification of autonomous robots' (2024, cited 16 times), 'Hierarchical Distribution-Aware Testing of Deep Learning' (2023, cited 19 times), 'Adversarial Training for Probabilistic Robustness' (2025), 'Hyper-CycleGAN: A New Adversarial Neural Network' (2025), and 'A case study of STPA using ChatGPT' (2025, cited 42 times). In January 2025, he received the prestigious EPSRC New Investigator Award for the project 'Harnessing Synthetic Data Fidelity for Assured Perception of Autonomous Vehicles'. With over 2,800 citations on Google Scholar and expertise in formal verification, reliability engineering, and software testing, Zhao significantly influences the field of safe AI. His work contributes to dependable autonomous systems through innovative approaches to testing and verification.