Zhijie Xie
PhD (Conferral Date: 31st March, 2026)
I am actively looking for a PostDoc position starting from March 2026.
I received the Ph.D. degree in Electronic and Computer Engineering (ECE) from The Hong Kong University of Science and Technology (HKUST), where I was adviced by Prof. Shenghui Song in 2026. Prior to that, I received the B.E. degree in Software Engineering from Tongji University in 2017. My research interests include:
- Federated Learning (FL), especially data heterogeneity, privacy concerns, communication efficiency, and convergence analysis.
- Reinforcement Learning (RL), especially policy optimization and RL theory.
- Trustworthy and Distributed Reinforcement Learning, specifically Federated Reinforcement Learning (FRL). Focusing on the Weighted Value Problems (WVPs) and Environment Heterogeneity. I am particularly interested in establishing the theoretical foundation of FRL and delivering algorithms with rigorous mathematical guarantees.
Prior to HKUST, I had been working with Prof. Yi Xing on researching and developing high-performance alternative splicing analysis algorithms for large-scale RNA-Seq data.
Federated Learning & Reinforcement Learning
Z. Xie and S. Song. The Actor-Critic update order matters for PPO in federated reinforcement learning, arXiv preprint arXiv:2506.01261, 2025.
Z. Xie and S. Song. Client selection for federated policy optimization with environment heterogeneity, to appear Journal of Machine Learning Research, 2025.
Z. Xie and S. Song. FedKL: Tackling data heterogeneity in federated reinforcement learning by penalizing KL divergence, in IEEE Journal on Selected Areas in Communications, vol. 41, no. 4, pp. 1227-1242, April 2023, doi: 10.1109/JSAC.2023.3242734.
G. Zhou, L. Zhao, G. Zheng, Z. Xie, S. Song and K. -C. Chen. Joint multi-objective optimization for radio access network slicing using multi-agent deep reinforcement learning, in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2023.3268671, 2023.
Wireless Communication
F. Xu, Z. Xie, S. Song et al. Multi-antenna UAV assisted hybrid FSO/RF data collection for IoT: Optimal design for fairness, in IEEE Transactions on Aerospace and Electronic Systems, doi: 10.1109/TAES.2025.3572070, 2025.
Bioinformatics
Wang Y*, Xie Z*, Kutschera E, Adams JI, Kadash-Edmondson KE, and Xing Y. rMATS-turbo: An efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data. Nature Protocols, doi:10.1038/s41596-023-00944-2, 2024. (*joint first authors)
Demirdjian L, Xu Y, Bahrami-Samani E, Pan Y, Pan Z, Stein S, Xie Z, Park E, Wu YN, Xing Y. Detecting allele-specific alternative splicing from population-scale RNA-seq data. American Journal of Human Genetics, 107:461-72, 2020.
Phillips JW*, Pan Y*, Tsai BL, Xie Z, Demirdjian L, Xiao W, Yang HT, Zhang Y, Lin CH, Cheng D, Hu Q, Liu S, Black DL, Witte ON+, Xing Y.+. Pathway-guided analysis identifies Myc-dependent alternative pre-mRNA splicing in aggressive prostate cancers. PNAS, 117(10):5269-79, 2020. (+joint corresponding authors; *joint first authors)
Zhang Z*, Pan Z*, Ying Y, Xie Z, Adhikari S, Phillips J, Carstens RP, Black DL, Wu Y, Xing Y. Deep learning-augmented RNA-seq analysis of transcript splicing. Nature Methods, 16:307-10, 2019. (*joint first authors)