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Curriculum Vitae of Xinyi Zhang. Download the PDF version.
Contact Information
| Name | ZHANG Xinyi |
| Professional Title | Senior Research Assistant |
| csxyzhang@comp.hkbu.edu.hk | |
| Phone | +852-56027517 |
| Location | DLB 625G, 34 Renfrew Rd, HKBU, Kowloon Tong, Hong Kong SAR |
| Website | https://linekm.github.io |
Professional Summary
Senior Research Assistant in the Department of Computer Science at Hong Kong Baptist University, with experience in designing and implementing high-performance, secure database systems and distribution-aware data structures.
Experience
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2026 - Present Hong Kong SAR
Senior Research Assistant
Hong Kong Baptist University
Working with Prof. Jianliang Xu.
- Research on efficient and secure query processing for relational and vector databases.
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2026 - 2026 Singapore
Visiting Scholar
Nanyang Technological University
Hosted by Dr. Qichen Wang.
- Worked on “Learned Indexes for Query Optimization”, integrating learned index structures into structure-guided execution plans for acyclic queries.
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2021 - 2026 Hong Kong SAR
PhD Candidate
Hong Kong Baptist University
Advised by Prof. Jianliang Xu.
- Designed novel algorithms and indexes for relational and vector databases to support efficient and secure query processing in a wide range of systems.
- Designed novel distribution-aware data structures to handle complex database workloads while ensuring robust performance.
- Research resulted in publications at top-tier conferences.
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2019 - 2020 Wuhan, China
Researcher
Huazhong University of Science and Technology
- Designed a fast consensus algorithm for blockchain combining SDN, dPOS and PBFT consensus algorithms.
- Built a practical blockchain system for ASTRI as an integrity-assured record for their public distributed database.
Education
Awards
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2024 VLDB Student Travel Award
VLDB Endowment
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2024, 2026 ACM SIGMOD Student Travel Award
ACM
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2024 RPg Research Performance Award
Hong Kong Baptist University
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2022 Excellent Teaching Assistant Performance Award
Hong Kong Baptist University
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2018, 2021 Academic Excellence Scholarship
Huazhong University of Science and Technology
Publications
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2026 Authenticated Approximate Nearest Neighbor Search in Outsourced Vector Databases
Under SIGMOD Revision
Y. Zhou†, X. Zhang†, C. Xu, and J. Xu. Full Paper. († These authors contributed equally.)
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2026 Privacy-Preserving Approximate Nearest Neighbor Search in Trusted Execution Environments
Under Review
Y. Peng†, X. Zhang†, J. Yang, S. Wang, T. N. Chan, B. Du, C. Dong, and J. Xu. Full Paper. († These authors contributed equally.)
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2026 HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads
Proceedings of the ACM on Management of Data (SIGMOD '26), vol. 4, no. 1
X. Zhang, L. Liang, A. Ailamaki, and J. Xu. Full Paper.
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2025 EDGE: DBMS-Empowered Boolean Decomposition for GIG Synthesis
ACM/IEEE Design Automation Conference (DAC '25), IEEE, pp. 1–7
R. Tang, X. Zhu, X. Zhang, L. Chen, X. Li, M. Yuan, and J. Xu. Full Paper.
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2024 FedKNN: Secure Federated k-Nearest Neighbor Search
Proceedings of the ACM on Management of Data (SIGMOD '24), vol. 2, no. 1
X. Zhang, Q. Wang, C. Xu, Y. Peng, and J. Xu. Full Paper.
Skills
Languages
Interests
Projects
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FedKNN: Secure Federated k-Nearest Neighbor Search (SIGMOD '24)
A system for secure and privacy-preserving federated kNN search, achieving up to 4.8× efficiency improvement over SOTA by optimizing local computations with a distribution-aware algorithm.
- Secure federated similarity search
- Distribution-aware local computation
- Up to 4.8× efficiency improvement over SOTA
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HIRE: A Hybrid Learned Index for Robust and Efficient Performance (SIGMOD '26)
A hybrid in-memory index combining learned predictions with traditional robustness, which reduces tail latency by 98% and improves throughput by 41.7× compared to SOTA approaches.
- Hybrid learned + traditional index design
- 98% tail-latency reduction
- 41.7× throughput improvement
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Privacy-Preserving Approximate Nearest Neighbor Search (Under Review)
A secure ANN index structure within trusted execution environments, mitigating side-channel attacks by enforcing oblivious memory access patterns on proximity graph indexes. Achieves plaintext-level throughput with provable privacy guarantees via a distribution-aware oblivious access algorithm.
- Oblivious proximity-graph access in TEE
- Plaintext-level throughput with provable privacy
- Distribution-aware oblivious algorithm
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Authenticated Approximate Nearest Neighbor Search (Under Revision, SIGMOD)
VIDA, a novel scheme for authenticated graph-based ANN search that leverages vector commitments and Inner Product Arguments to verify distance computations, drastically reducing the overall data transmission size. MeST, a proof aggregation mechanism, compresses the verification of multiple vectors into a single cryptographic proof. Up to 5.2× speedup in end-to-end latency and 99.3% reduction in data transfer time.
- Vector commitments + Inner Product Arguments
- MeST proof aggregation
- Up to 5.2× end-to-end speedup; 99.3% data-transfer reduction
References
- Prof. Xu Jianliang — Head & Chair Professor, Hong Kong Baptist University
- Dr. Wang Qichen — Assistant Professor, Nanyang Technological University
- Prof. Byron Choi — Associate Head & Professor, Hong Kong Baptist University