- Assistant Professor, Applied Computing
- Assistant Professor, Computer Science
- Ph.D., Computer Science, University of Florida, 2020
- M.E., Software Engineering, Peking University, 2015
- B.S., Mathematics, Fudan University, 2012
Xiaoyong (Brian) Yuan's research interests span the fields of deep learning, machine learning, security and privacy, and cloud computing. He has published papers in top-tier journals and conference proceedings, such as IEEE Transactions on Neural Networks and Learning Systems (TNNLS) and the AAAI Conference on Artificial Intelligence. And he has served as reviewer for several leading journals and conferences, such as IEEE Transactions on Neural Networks and Learning Systems (TNNLS), International Conference on Learning Representations (ICLR), IEEE Transactions on Dependable and Secure Computing (TDSC), and IEEE Transactions on Parallel and Distributed Systems (TPDS).
Links of Interest
Areas of Expertise
- Machine Learning
- Security and Privacy
- Cloud Computing
- Xiaoyong Yuan, Xiyao Ma, Lan Zhang, Yuguang Fang, Dapeng Wu, "Beyond Class-Level Privacy Leakage: Breaking Record-Level Privacy in Federated Learning," IEEE Internet of Things Journal, 2021.
- Xiaoyong Yuan, Lei Ding, Malek Ben Salem, Xiaolin Li, Dapeng Wu, “Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks,” EAI SecureComm, 2020.
- Ruimin Sun, Marcus Botacin, Nikolaos Sapountzis, Xiaoyong Yuan, Matt Bishop, Donald E Porter, Xiaolin Li, Andre Gregio, Daniela Oliveira, “A Praise for Defensive Programming: LeveragingUncertainty for Effective Malware Mitigation,” IEEE Transactions on Dependable and Secure Computing (TDSC), 2020.
- Xiaoyong Yuan, Pan He, Xiaolin Li, “Adaptive Adversarial Attack on Scene Text Recognition,” The 8th International Workshop on Security and Privacy in Big Data (BigSecurity 2020), INFOCOM, 2020.
- Xiaoyong Yuan, Pan He, Qile Zhu, Xiaolin Li, “Adversarial Examples: Attacks and Defenses for Deep Learning,” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019.
- Xiaoyong Yuan*, Zheng Feng*, Matthew Norton, Xiaolin Li, “Generalized Batch Normalization: Towards Accelerating Deep Neural Networks,” AAAI Conference on Artificial Intelligence (AAAI), 2019.
- Yinan Zhao, Jian Ge, Xiaoyong Yuan, Tiffany Zhao, Cindy Wang, Xiaolin Li, “Identifying Mg ii narrow absorption lines with deep learning,” Monthly Notices of the Royal Astronomical Society, 2019.
- Chuanhuang Li, Yan Wu, Xiaoyong Yuan, Zhengjun Sun, Weiming Wang, Xiaolin Li, Liang Gong, “Detection and defense of DDoS attack–based on deep learning in OpenFlow-based SDN,” International Journal of Communication Systems, 2018.
- Xiaoyong Yuan, Chuanhuang Li, Xiaolin Li, “DeepDefense: Identifying DDoS Attack via Deep Learning,” IEEE International Conference on Smart Computing (SMARTCOMP), 2017.
- Ruimin Sun, Xiaoyong Yuan, Andrew Lee, Matt Bishop, Donald E. Porter, Xiaolin Li, Andre Gregio and Daniela Oliveira, “The Dose Makes the Poison - Leveraging Uncertainty for Effective Malware Detection,” IEEE Conference on Dependable and Secure Computing (DSC), 2017.
- Xiaoyong Yuan, Min Li, Sudeep Gaddam, Xiaolin Li, Yinan Zhao, Jingzhe Ma, Jian Ge, “DeepSky: Identifying Absorption Bumps via Deep Learning,” IEEE International Congress on Big Data (BigData Congress), 2016.
- Xiaoyong Yuan, Long Wang, Tiancheng Liu, Yue Zhang, “A Methodology for Continuous Evaluation of Cloud Resiliency,” American Journal of Engineering and Applied Sciences, 2016.
- Hongyan Tang, Ying Li, Tong Jia, Xiaoyong Yuan, Zhonghai Wu, “Time Series Based Killer Task Online Recognition Service: A Google Cluster Case Study,” International Conference on Service-Oriented System Engineering (SOSE), 2016.
- Xiaoyong Yuan, Hongyan Tang, Ying Li, Tong Jia, Tiancheng Liu, Zhonghai Wu, “A Competitive Penalty Model for Availability Based Cloud SLA,” IEEE International Conference on Cloud Computing (CLOUD), 2015.
- Xiaoyong Yuan, Ying Li, Tong Jia, Tiancheng Liu, Zhonghai Wu, “An Analysis on Availability Commitment and Penalty in Cloud SLA,” Annual International Computers, Software & Applications Conference (COMPSAC), 2015.
- Xiaoyong Yuan, Ying Li, Zhonghai Wu, Tiancheng Liu, “Dependability Analysis on OpenStack IaaS Cloud: Bug Analysis and Fault Injection,” IEEE International Conference on Cloud Computing Technology and Science (CLOUDCOM), 2014.
- Collaborative Research: SHF: Medium: Heterogeneous Architecture for Collaborative Machine Learning (~$400K), National Science Foundation, 2021-2024, PI
- Research Excellence Fund (REF) Award ($29.9K), Michigan Technological University, 2021-2022, PI