VLDB 2026 Research / reviewers in the wild / expert
Shuangyue Zhang
dblp:144/4742
· DBLP profile ↗
3ranked-venue papers
1as first author
1since 2021 · last 2023
0009-0000-4502-2463ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-authorSecurity and privacy · 1Software engineering, systems software and programming languages · 1 · 1 first-authorTheory of computation · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Generation matrix: An embeddable matrix representation for hierarchical trees
Ximeng Liu, Shuangyue Zhang |
Theor. Comput. Sci. | 4 |
| 2017 | Perturbation Paradigms of Maintaining Privacy-Preserving Monotonicity for Differential Privacy
Hai Liu 0007, Zhenqiang Wu, Changgen Peng, Shuangyue Zhang, Feng Tian 0004, Laifeng Lu |
ICICS | 4 |
| 2014 | Joint Virtual Probe: Joint exploration of multiple test items' spatial patterns for efficient silicon characterization and test predictionabstractVirtual Probe (VP), proposed for characterization of spatial variations and for test time reduction, can effectively reconstruct the spatial pattern of a test item for an entire wafer using measurement values from only a small fraction of dies on the wafer. However, VP calculates the spatial signature of each test item separately, one item at a time, resulting in very long runtime for complex chips which often require hundreds, or even thousands, of test items in production. In this paper, we propose a new method, named Joint Virtual Probe (JVP), which can jointly derive spatial patterns of multiple test items. By simultaneously handling a large group of test items, JVP significantly reduces the overall runtime. And the prediction accuracy can also be improved because of JVP's implicit use of inter-test-item correlations in predicting spatial patterns. The experimental results on two industrial products, with 277 and 985 parametric test items in the production test programs respectively, demonstrate that, JVP achieves an average speedup of ~ 170X and ~ 50X over VP in the pre-test analysis and the test application phases respectively, as well as a slightly higher prediction accuracy than VP. Shuangyue Zhang, Fan Lin, Chun-Kai Hsu, Kwang-Ting Cheng |
DATE | 1 |