About Me
I am a Ph.D. student in Computer Science at the School of Information and the Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China, advised by Prof. Feng Zhang and Prof. Xiaoyong Du. I received my bachelor’s degree in Data Science and Big Data Technology from Renmin University of China in 2022. During my Ph.D. study, I have visited North Carolina State University, working with Prof. Xipeng Shen, and National University of Singapore, working with Prof. Bingsheng He.
My research lies at the intersection of data compression and high-performance AI systems. I am particularly interested in designing compression-aware algorithms and systems that reduce storage, memory, and computation costs while preserving practical performance on real workloads. My recent work studies compressed-domain text and graph processing, efficient neural network inference on edge devices, and KV cache compression/quantization for large language model training/inference.
Research Interests
- Data compression: compressed-domain computing, homomorphic compression for text processing, rule-based graph compression, time-series compression, and database systems that operate directly on compressed data.
- High-performance AI systems: efficient neural network inference, KV cache compression and quantization, LLM inference optimization, edge-device acceleration, and hardware/software co-designed runtime systems.
News
- 🇸🇬 2026.03: Started a visiting scholar appointment at National University of Singapore, working on low-bit KV cache quantization and hardware/software co-designed LLM inference optimization.
- 🎉 2026.02: Two papers were accepted to SIGMOD 2026, on efficient updates for rule-based compressed graphs and GPU-native learned lossless lightweight compression.
- 🌟 2025.11: Awarded the NSFC Basic Research Scheme for PhD Students as principal investigator.
- 🌟 2025.11: Selected for the Young Science and Technology Scientists Sponsorship Program by CAST - Doctoral Student Special Plan.
- 🏅 2025.04: Received the Gold Medal with the Congratulations of the Jury at the Geneva International Exhibition of Inventions.
Publications
Enabling Efficient Update on Rule-Based Compressed Graph
Lin Feng, Feng Zhang, Zheng Chen, Yuxin Tang, Jiawei Guan, Xiaowei Zhu, Xiaoyong Du
ACM Conference on Management of Data (SIGMOD), 2026
L3: A GPU-Native Co-Designed Data Format for Learned Lossless Lightweight Compression
Youyang Xia, Feng Zhang, Junda Pan, Yihao Liu, Jiawei Guan, Huanchen Zhang, Xiaoyong Du
ACM Conference on Management of Data (SIGMOD), 2026
Compressed Semantic Retrieval as External Memory for Scalable AI Systems
Jiawei Guan, Yancong Wang, Feng Zhang, Qiang Yin, Wei Wang, Xiaoyong Du
ICML Workshop, 2026
A Systematic Study on Early Stop Metrics in HPO and the Implications of Uncertainty
Jiawei Guan, Feng Zhang, Jiesong Liu, Xipeng Shen
International Conference on Very Large Data Bases (VLDB), 2025
Improving Time Series Data Compression in Apache IoTDB
Yuxin Tang, Feng Zhang, Jiawei Guan, Yuan Tian, Xiangdong Huang, Chen Wang, Jianmin Wang, Xiaoyong Du
International Conference on Very Large Data Bases (VLDB), 2025
Breaking the Edge: Enabling Efficient Neural Network Inference on Integrated Edge Devices
Feng Zhang, Chenyang Zhang, Jiawei Guan, Qiangjun Zhou, Kuangyu Chen, Xiao Zhang, Bingsheng He, Jidong Zhai, Xiaoyong Du
TCC, 2025
Homomorphic Compression: Making Text Processing on Compression
Jiawei Guan, Feng Zhang, Siqi Ma, Kuangyu Chen, Yihua Hu, Yuxing Chen, Anqun Pan, Xiaoyong Du
ACM Conference on Management of Data (SIGMOD), 2024. CCF A.
Improving Graph Compression for Efficient Resource-Constrained Graph Analytics
Qian Xu, Juan Yang, Feng Zhang, Zheng Chen, Jiawei Guan, Kang Chen, Ju Fan, Youren Shen, Ke Yang, Yu Zhang, Xiaoyong Du
Proceedings of the VLDB Endowment (PVLDB), 2024. CCF A.
HocoPG: A Database System with Homomorphic Compression for Text Processing
Jiawei Guan, Feng Zhang, Yuxin Tang, Weitang Ye, Xiaoyong Du
VLDB Demo Track, 2024. CCF A.
UQ-Guided Hyperparameter Optimization for Iterative Learners
Jiesong Liu, Feng Zhang, Jiawei Guan, Xipeng Shen
Conference on Neural Information Processing Systems (NeurIPS), 2024. CCF A.
Enabling Efficient Deep Learning on MCU with Transient Redundancy Elimination
Jiesong Liu, Feng Zhang, Jiawei Guan, Hsing-Hsuan Sung, Xiaoyong Du, Xipeng Shen
IEEE Transactions on Computers (TC), 2024. CCF A.
Space-Efficient TREC for Enabling Deep Learning on Microcontrollers
Jiesong Liu, Feng Zhang, Jiawei Guan, Hsin-Hsuan Sung, Xiaoguang Guo, Xiaoyong Du, Xipeng Shen
Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2023. CCF A.
CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression
Zheng Chen, Feng Zhang, Jiawei Guan, Jidong Zhai, Xipeng Shen, Huanchen Zhang, Wentong Shu, Xiaoyong Du
ACM Conference on Management of Data (SIGMOD), 2023. CCF A.
Expanding the Edge: Enabling Efficient Winograd CNN Inference With Deep Reuse on Edge Device
Feng Zhang, Ruofan Wu, Jiawei Guan, Zhen Zheng, Xiaoguang Guo, Xiao Zhang, Xiaoyong Du, Xipeng Shen
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. CCF A.
TREC: Transient Redundancy Elimination-based Convolution
Jiawei Guan, Feng Zhang, Jiesong Liu, Hsin-Hsuan Sung, Ruofan Wu, Xiaoyong Du, Xipeng Shen
Conference on Neural Information Processing Systems (NeurIPS), 2022. CCF A. Acceptance rate: 25.6% (2,672/10,411).
DREW: Efficient Winograd CNN Inference with Deep Reuse
Ruofan Wu, Feng Zhang, Jiawei Guan, Zhen Zheng, Xiaoyong Du, Xipeng Shen
The Web Conference (WWW), 2022. CCF A. Acceptance rate: 17.7% (323/1,822).
Honors and Awards
- NSFC Basic Research Scheme for PhD Students, Principal Investigator, 2025–2027.
- Young Science and Technology Scientists Sponsorship Program by CAST - Doctoral Student Special Plan, 2025.
- Gold Medal with the Congratulations of the Jury (top 5%), Geneva International Exhibition of Inventions, 2025.
- China National Scholarship, 2025.
- Graduate Academic Excellence Scholarship, First Class, Renmin University of China, 2024.
- Outstanding Innovative Talents Award, Renmin University of China, 2023.
- Outstanding Graduate of Beijing, 2022.
- Huawei Kunpeng Scholarship, Renmin University of China, 2021.
Education
- Ph.D. in Computer Science, Renmin University of China, Sep. 2022 – Present. Advisor: Prof. Feng Zhang.
- B.S. in Data Science and Big Data Technology, Renmin University of China, Sep. 2018 – Jun. 2022.
Internship Experience
- Algorithm Intern, Volcano Engine AI Cloud Search, ByteDance, Oct. 2025 – Jun. 2026. Worked on category-aware Reranker model design for large-scale search re-ranking. The project models semantic type, similarity, and matching relationships between queries and documents to improve ranking discrimination for complex queries and cross-category retrieval scenarios.
Visiting Experience
- National University of Singapore, Visiting Scholar, Mar. 2026 – Jul. 2026. Advisor: Prof. Bingsheng He. Research on low-bit KV cache quantization and hardware/software co-designed LLM inference optimization.
- North Carolina State University, Visiting Scholar, Jan. 2025 – Aug. 2025. Advisor: Prof. Xipeng Shen. Research on data compression and high-performance AI.