🔍 Research Interests
Zhongle’s research lives at the intersection of data systems and artificial intelligence, where he spends most of his time trying to make AI more efficient, more usable, and a little less expensive to run. He is interested in how learning and inference workloads interact with data and system infrastructures, and in developing techniques that improve model efficiency, data utilization, and overall system performance.
Zhongle takes a data-centric and system-aware view of AI, which means he often looks for efficiency gains in places other than the model itself. Instead of redesigning neural networks, he focuses on how data is organized, accessed, and moved, and how system-level decisions quietly dominate performance. Many of his research questions boil down to a simple theme: how to make AI workloads run faster, cheaper, and more reliably by being smarter about data and systems.
Earlier in his career, Zhongle focused on efficiency and integrity in analytical systems. He later spent nearly four years wandering into startup environments, a memorable period that shaped his perspective on real systems, while gently slowing academic momentum. Along the way, he guided dozens of system and data engineers, reinforcing his belief that good systems must be usable, correct, and efficient in practice.
📝 Selected Publications
Benefiting from a system research perspective, Zhongle maintains sustained collaborations with industry (marked with 🔖 or ), and often acts closely involved in every paper that carries his name.
Conference
[C22] C Lv, H Li, D Yang, Z Xie, L Chen, CS Jensen. DeXOR: Enabling xor in Decimal Space for Streaming Lossless Compression of Floating-point Data. VLDB 2026
[C21] Y Peng, D Yang, Z Xie, J Sun, L Shou, K Chen, G Chen. SVFusion: A CPU-GPU Co-Processing Architecture for Large-Scale Real-Time Vector Search. VLDB 2026 🔖
[C20] Y Wu, et al.. SafeLoad: Efficient Admission Control Framework for Identifying Memory-Overloading Queries in Cloud Data Warehouses. VLDB 2026 🔖
[C19] S Wu, et al.. MorphingDB: A Task-Centric AI-Native DBMS for Model Management and Inference. SIGMOD 2026
Journal
[J4] X Chen, Z Xie*, H Li, K Chen, L Shou, D Jiang, G Chen. PIMSHARE: Scheduling for Multi-DNN Inference on Processing-in-memory Accelerated Edge Server. IEEE TCAD 2026 (To Appear)
Conference
[C18] H Lin, S Wan, Z Xie*, K Chen*, M Zhang, L Shou, G Chen. A Comprehensive Study of Shapley Value in Data Analytics. VLDB 2025
[C17] G Hu, S Cai, TTA Dinh, Z Xie*, C Yue, G Chen, BC Ooi. HAKES: Scalable Vector Database for Embedding Search Service. VLDB 2025
[C16] Z Ji, X Wang, Z Luo, Z Xie, M Zhang. Optimized Batch Prompting for Cost-effective LLMs. VLDB 2025
[C15] Y Zhou, Z Li, J Zhang, J Wang, Y Wang, Z Xie*, K Chen, L Shou*. FloE: On-the-Fly MoE Inference on Memory-constrained GPU. ICML 2025
[C14] Y Peng, Z Xie*, K Chen*, G Chen, L Shou. Towards Automatic and Efficient Prediction Query Processing in Analytical Database. IEEE ICDE 2025
Journal
[J3] P Lu, Z Xie*, D Jiang, K Chen, L Shou. Cohort query processing without misleading aging effects. The VLDB Journal
[J2] J Zhang, J Wang, H Li, Z Xie, K Chen, L Shou. CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning. IEEE TKDE 2025
Conference
[C13] Best RunnerUp Paper! Z Ji, Z Xie, Y Wu, M Zhang. LBSC: A Cost-Aware Caching Framework for Cloud Databases. IEEE ICDE 2024
Conference
[C12] C Yue, TTA Dinh, Z Xie, M Zhang, G Chen, BC Ooi, X Xiao. GlassDB: An efficient verifiable ledger database system through transparency. VLDB 2023
[C11] Y Ma, Z Xie, J Wang, K Chen, L Shou. Continual Federated Learning Based on Knowledge Distillation. IJCAI 2022
[C10] J Zhang, S Wu, J Zhao, Z Xie, F Li, Y Gao, G Chen. A sampling-based learning framework for big databases. WWW 2022 🔖
[C9] M Zhang, Z Xie, C Yue, Z Zhong. Spitz: A verifiable database system. VLDB 2020
[C8] C Yue, Z Xie, M Zhang, G Chen, BC Ooi, S Wang, X Xiao. Analysis of indexing structures for immutable data. ACM SIGMOD 2020
[C7] Z Xie, H Ying, C Yue, M Zhang, G Chen, BC Ooi. Cool, a COhort OnLine analytical processing system. IEEE ICDE 2020
[C6] Z Xie, Q Cai, F He, GY Ooi, W Huang, BC Ooi. Cohort analysis with ease. ACM SIGMOD 2018 Demo.
[C5] Z Xie, Q Cai, G Chen, R Mao, M Zhang: A comprehensive performance evaluation of modern in-memory indices. IEEE ICDE 2018
[C4] Q Cai, Z Xie, M Zhang, G Chen, HV Jagadish, BC Ooi. Effective temporal dependence discovery in time series data. VLDB 2018
[C3] S Wang, TTA Dinh, Q Lin, Z Xie, M Zhang, Q Cai, G Chen, W Fu, BC Ooi, P Ruan. Forkbase: An efficient storage engine for blockchain and forkable applications. VLDB 2018
[C2] Z Xie, Q Cai, HV Jagadish, BC Ooi, WF Wong. Parallelizing skip lists for in-memory multi-core database systems. IEEE ICDE 2017
[C1] BC Ooi, et al.. SINGA: A distributed deep learning platform. ACM MM 2015
Journal
[J1] Q Cai, C Cui, Y Xiong, W Wang, Z Xie, M Zhang. A survey on deep reinforcement learning for data processing and analytics. IEEE TKDE 2022
🏆 Awards and Honors
- Sep 2025 CCF Science and Technology Achievement Award - Natural Science Award (Second Prize)
- June 2024 SIGMOD Systems Award
- April 2024 ICDE Best RunnerUp Paper
- Aug 2024 VLDB Distinguished Reviwer
📖 Educations
- 2014.08 - 2020.01, National University of Singapore, Ph.D., supervised by Prof. Beng Chin OOI.
- 2010.09 - 2014.06, Shanghai Jiao Tong University, Bachelor, supervised by Prof. Bin YAO.
🧙 Professional Activities
- VLDB Review Board/PC Member: 2022, 2023, 2024, 2026
- ICDE PC Member: 2023
- EDBT PC Member: 2026
- CIKM PC Member: 2025