PhD student @ Purdue CS
I am a PhD student of Computer Science Department, Purdue University. My advisor is Suresh Jagannathan. I am working on attack/defense/assurance of deep learning, especially deep reinforcement learning, from the perspective of formal methods. I received my B.Eng. of Software Engineering from University of Electronic Science and Technology of China in 2018.
CV updated at 2021-04-07.
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Although deep neural networks have achieved promising performance in various tasks, they are generally used as black-box functions without any formal guarantee on their properties. For example, without formal analysis, it is unclear whether a neural-network-controlled drone, which is operated in a complex environment, will collide with the ground or not. This line of work provided verifiable safety guarantee for cyber-physical-systems (e.g., robots, UVA) trained with deep reinforcement learning.
Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning
Zikang Xiong, and Suresh Jagannathan.
4th Workshop on Formal Methods for ML-Enabled Autonomous Systems [pdf]
Neural network controllers are not robust to adversarial attacks, which exposes them to great threats from malicious attackers. We aim to explore both attack and defense techniques for deep-neural-network controlled systems, thus providing more robust neural network controllers.
Baidu Autonomous Driving Unit (Apollo), Path Planning Optimization, 06/2021 - 09/2021.