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) under the supervision of 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.
- The integration of RL and FL, i.e., Federated Reinforcement Learning (FRL). Focusing on the Weighted Value Problems (WVPs) and Environment Heterogeneity. I am particularly intereseted in establishing the theoretical foundation of FRL.
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)