Nargiz Humbatova
Papers
1
Total Citations
4
H-Index
1
About
Nargiz Humbatova is a leading researcher in the field of software engineering for artificial intelligence, with a primary focus on testing and validation of deep learning and reinforcement learning (RL) systems. Her most notable contribution is the development of μPRL, a mutation testing pipeline specifically designed for deep reinforcement learning agents, which leverages real-world faults to assess test suite adequacy. This work addresses a critical gap in ensuring the reliability of RL-based systems deployed in high-stakes domains such as autonomous driving and robotics. With her highly cited paper already garnering attention, Humbatova’s research is pivotal in advancing systematic verification methods for AI. Her achievements highlight a deep commitment to bridging the gap between traditional software testing and modern AI paradigms, making her a key figure in the emerging area of AI reliability engineering.
Research Focus
Key Achievements
Top Papers
- 1