Profile
| Keywords: | Software Engineering, Machine Learning System Engineering, Cyber-Physical Systems |
Dr. Lei Ma is currently an Associate professor and Canada CIFAR AI Chair at the University of Alberta, leading Momentum Intelligent System Engineering Lab. He is also affiliated with Alberta Machine Intelligent Institute as an Amii Fellow and the committee member of ISO/IEC JTC 1/SC 42 Artificial intelligence, on AI System Engineering.
He received his Ph.D. and M.E. from the University of Tokyo, and B.E. from Shanghai Jiao Tong University. His research centers around the interdisciplinary fields of Software Engineering (SE), Security and Trustworthy AI with a special focus on the quality, reliability and security assurance of machine learning and AI solutions. Many of his work were published in top-tier SE, AI and security venues (TSE, TOSEM, ICSE, FSE, ASE, ISSTA, ICML, NeurIPS, TNNLS, ACM MM, AAAI, IJCAI, ECCV, CAV, CCS, TDSC). He is a recipient of more than 10 prestigious academic awards, including 3 ACM SIGSOFT Distinguished Paper Awards. Many of his research innovations for secure and reliable AI are adopted by industry worldwide.
More detailed information could found at his personal website: https://www.malei.xyz FES Funded ProjectsOutputs
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| When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way ForwardCyber-physical systems (CPS) have been broadly deployed in safety-critical domains, such as automotive systems, avionics, medical devices, etc. In recent years, Artificial Intelligence (AI) has been increasingly adopted to control CPS. Despite the popularity of AI-enabled CPS, few benchmarks are publicly available. There is also a lack of deep understanding on the performance and reliability of AI-enabled CPS across different industrial domains. To bridge this gap, we initiate to create a public benchmark of industry-level CPS in seven domains and build AI controllers for them via state-of-the-art deep reinforcement learning (DRL) methods. Based on that, we further perform a systematic evaluation of these AI-enabled systems with their traditional counterparts to identify the current challenges and explore future opportunities. Our key findings include (1) AI controllers do not always outperform traditional controllers, (2) existing CPS testing techniques (falsification, specifically) fall short of analyzing AI-enabled CPS, and (3) building a hybrid system that strategically combines and switches between AI controllers and traditional controllers can achieve better performance across different domains. Our results highlight the need for new testing techniques for AI-enabled CPS and the need for more investigations into hybrid CPS systems to achieve optimal performance and reliability.
T13-Q02 University of Alberta | Publication | 2022-05-08 | Song, J., "Deyun Lyu ", "Zhenya Zhang ", "Zhijie Wang ", "Tianyi Zhang ", Ma, L. | | SIEGE: A Semantics-Guided Safety Enhancement Framework for AI-enabled Cyber-Physical SystemsCyber-Physical Systems (CPSs) have been widely adopted in various industry domains to support many important tasks that impact our daily lives, such as automotive vehicles, robotics manufacturing, and energy systems. As Artificial Intelligence (AI) has demonstrated its promising abilities in diverse tasks like decision-making, prediction, and optimization, a growing number of CPSs adopt AI components in the loop to further extend their efficiency and performance. However, these modern AI-enabled CPSs have to tackle pivotal problems that the AI-enabled control systems might need to compensate the balance across multiple operation requirements and avoid possible defections in advance to safeguard human lives and properties. Modular redundancy and ensemble method are two widely adopted solutions in the traditional CPSs and AI communities to enhance the functionality and flexibility of a system. Nevertheless, there is a lack of deep understanding of the effectiveness of such ensemble design on AI-CPSs across diverse industrial applications. Considering the complexity of AI-CPSs, existing ensemble methods fall short of handling such huge state space and sophisticated system dynamics. Furthermore, an ideal control solution should consider the multiple system specifications in real-time and avoid erroneous behaviors beforehand. Such that, a new specification-oriented ensemble control system is of urgent need for AI-CPSs.
In this paper, we propose SIEGE, a semantics-guided ensemble control framework to initiate an early exploratory study of ensemble methods on AI-CPSs and aim to construct an efficient, robust, and reliable control solution for multi-tasks AI-CPSs. We first utilize a semantic-based abstraction to decompose the large state space, capture the ongoing system status and predict future conditions in terms of the satisfaction of specifications. We propose a series of new semantics-aware ensemble strategies and an end-to-end Deep Reinforcement Learning (DRL) hierarchical ensemble method to improve the flexibility and reliability of the control systems. Our large-scale, comprehensive evaluations over five subject CPSs show that 1) the semantics abstraction can efficiently narrow the large state space and predict the semantics of incoming states, 2) our semantics-guided methods outperform state-of-the-art individual controllers and traditional ensemble methods, and 3) the DRL hierarchical ensemble approach shows promising capabilities to deliver a more robust, efficient, and safety-assured control system. T13-Q02 University of Alberta | Publication | 2022-11-04 | |
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