Xueying Liu

Education

• 2025 Ph.D. in Statistics, Virginia Tech, Blacksburg
• 2019 M.S. in Statistics, Georgia Institute of Technology, Atlanta, GA
• 2018 B.S. in Mathematics, Shanghai University, Shanghai, China

Research

• Modeling complex data and uncertainty quantification 
• Statistical and network modeling with application to social and behavioral science 
• Multi-task learning for clinical trial data 
• Statistics-guided continual learning for artificial intelligence

Publications

Peer-Reviewed Journal Articles 

1. Liu, X., Hu, Z., Deng, X., and Kuhlman, C. J. (2023). Uncertainty visualization for characterizing heterogeneous human behaviors in discrete dynamical system models. Advances in Complex Systems, 26(3), pp. 2340001-1
2. Hu, Z., Liu, X., Deng, X., and Kuhlman, C. J. (2024). An uncertainty quantification framework for agent-based modeling and simulation in networked anagram games. Journal of Simulation, 1-19.
3. Chu, S., Liu, X., Marathe, A., and Deng, X. (2024). A latent process approach to change-point detection of mixed-type observations. Quality Engineering, 36(2), 407-426. 

Peer-Reviewed Conference Paper 

4. Liu, X., Hu, Z., Deng, X., and Kuhlman, C. J. (2022). A Bayesian uncertainty quantification approach for agent-based modeling of networked anagram games. Proceedings of the 2022 Winter Simulation Conference (WSC 2022), pp. 310-321. IEEE.
5. Liu, X., Hu, Z., Deng, X., and Kuhlman, C. J. (2022). Bayesian approach to uncertainty visualization of heterogeneous behaviors in modeling networked anagram games. Proceedings of the 2022 International Conference on Complex Networks and Their Applications (CNA 2022), pp. 595-607.
6. Liu, X., Hu, Z., Deng, X., and Kuhlman, C. J. (2023). A calibration model for bot-like behaviors in agent-based anagram game simulation. Proceedings of the 2023 Winter Simulation Conference (WSC 2023), pp. 221-232. IEEE.
7. Liu, X., Hu, Z., Deng, X., and Kuhlman, C. (2023). Learning common knowledge networks via exponential random graph models. Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2023). (acceptance rate 17%)
8. He, H., Liu, X., Kuhlman, C. and Deng, X. (2024). A framework of digital twins for modeling human-subject word formation experiments. Accepted by 2024 Winter Simulation Conference (WSC 2024).
9. He, H., Liu, X., et al. (2024). A successive analysis of online networked common knowledge experiments. Accepted by 2024 Advances in Social Networks Analysis and Mining (ASONAM 2024).
10. Lian, J., Liu, X., et al. (2024). Data composition for continual learning in application of cyberattack detection. Accepted by 2024 Advances in Social Networks Analysis and Mining (ASONAM 2024).