Dr. Xueping (Susan) Liang is an Assistant Professor in the Department of Information Systems and Business Analytics in Florida International University. She obtained her Ph.D. (2019) from University of Chinese Academy of Sciences, and B.S. (2013) from Beijing Institute of Technology.

Her research is centered around cybersecurity, Blockchain, AI/machine learning, data provenance mechanisms, and privacy protection. Specifically, she is interested in designing distributed consensus models in Blockchain technology, adopting AI for enhancing cyber-resiliency in healthcare and IoT, and adressing various practical security issues in cloud computing. She has published more than 30 conference and journals papers, book chapters at reputed venues. One of her papers has been awarded as “Top 50 Influential Papers in Blockchain” at BlockchainConnect Conference, San Francisco, in January 2019. Her research has been funded by the Office of Naval Research and NSF IUCRC.

Projects:

• Human-AI Collaboration in Cyber Defense
  • Brief Description: This project aims at enhancing cybersecurity resilience by leveraging the collective intelligence of human experts and the analytical power of artificial intelligence (AI) systems. Faced with increasingly sophisticated cyber threats, the project seeks to address the need for proactive and adaptive defense mechanisms, considering how to optimize the effectiveness and resilience in the human-AI collaboration process. It will integrate data from various sources, including network traffic, logs, and threat intelligence feeds, to provide a comprehensive view of the cyber landscape. Advanced machine learning and AI models will be developed to analyze data in real-time, identify anomalies, and make predictions about potential threats.  Human analysts will collaborate with AI systems to validate and refine threat assessments. Their expertise will guide AI training and decision-making. The project will implement feedback loops to ensure that AI models improve over time by learning from human feedback and evolving threat scenarios.

• The Design of Blockchain and AI for algorithmic fairness in healthcare
  • Brief Description: This project represents a pioneering effort to mitigate biases and inequities in healthcare decision-making by harnessing the transformative potential of blockchain technology and artificial intelligence (AI). In an era where data-driven healthcare decisions are increasingly prevalent, the project seeks to ensure that these decisions are not only accurate but also fair and unbiased. The primary objective of this project is to develop and implement innovative techniques that promote algorithmic fairness in healthcare AI systems. By leveraging blockchain’s transparency and immutability, the project aims to design AI algorithms that are inherently unbiased. The project will focus on identifying and mitigating biases that may exist in existing healthcare AI systems. It will also design mechanisms to prevent the introduction of new biases in data collection and algorithm development. Blockchain technology will be used to create an auditable record of healthcare decisions, ensuring transparency and accountability in AI-driven healthcare processes.