VLDB 2026 Research / reviewers in the wild / expert
Zhijing Zhang
dblp:22/6525
· DBLP profile ↗
5ranked-venue papers
0as first author
4since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 since 2021Databases, data management, data science and information retrieval · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Trustworthy machine learning · 87% Vision and language · 13% | |
| Software engineering, system software, and programming languages
1 paper |
Requirements engineering and software design · 77% Empirical software engineering · 23% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning
machine unlearning |
1.0 | 1 | 2026 | SGPVT: Self-Generated Proximal Visual Tokens for Mitigating Proximal Collateral Damage in MLLM Unlearning · ACL (1) 2026 |
Machine learning › Trustworthy machine learning › machine unlearning
multimodal large language model unlearning |
1.0 | 1 | 2026 | SGPVT: Self-Generated Proximal Visual Tokens for Mitigating Proximal Collateral Damage in MLLM Unlearning · ACL (1) 2026 |
Visualization and visual analytics
graph visualization |
1.0 | 1 | 2026 | CADModelScope: Revealing the Dependency Structure Behind Parametric Computer-Aided Design Models · CHI 2026 |
Requirements engineering and software design
computer-aided design |
1.0 | 1 | 2026 | CADModelScope: Revealing the Dependency Structure Behind Parametric Computer-Aided Design Models · CHI 2026 |
Computer vision › Vision and language › vision-language model
multimodal large language model |
0.3 | 1 | 2026 | SGPVT: Self-Generated Proximal Visual Tokens for Mitigating Proximal Collateral Damage in MLLM Unlearning · ACL (1) 2026 |
Empirical software engineering › developer studies › cognitive processes
model comprehension |
0.3 | 1 | 2026 | CADModelScope: Revealing the Dependency Structure Behind Parametric Computer-Aided Design Models · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
qualitative lab study · 2.0self-generated proximal visual tokens · 1.0proximal collateral damage mitigation · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | SGPVT: Self-Generated Proximal Visual Tokens for Mitigating Proximal Collateral Damage in MLLM UnlearningabstractJiaqi Li, Zhijing Zhang, Jiahui Geng, Sheng Bi, Chuanyi Zhang, Fan Liu, Guilin Qi. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026. Jiaqi Li 0031, Zhijing Zhang, Jiahui Geng, Chuanyi Zhang, Fan Liu 0003, Guilin Qi |
ACL (1) | 2 |
| 2026 | CADModelScope: Revealing the Dependency Structure Behind Parametric Computer-Aided Design ModelsabstractParametric computer-aided design (CAD) models are constructed by a sequence of operations, where each operation may reference geometries created by earlier operations. This network of dependencies enables efficient modelling of complex geometry but also results in fragile models, where small modifications can trigger cascading errors. These interdependencies are obscured in commercial CAD systems, leaving users to rely on trial and error when navigating, modularizing, and debugging unfamiliar and complex models. In this paper, we motivate, present, and pilot CADModelScope, a multi-level graph-based visualization of operation dependencies integrated into a commercial CAD platform. In a qualitative lab study, we observed how participants locate and interpret operations, and how CADModelScope enhances awareness of hidden interdependencies and supports more structured navigation. Our findings highlight the potential of using the network of operation dependency as an effective representation for understanding and interacting with parametric CAD models, and we discuss implications for future tool design. Yuanzhe Deng, Zhijing Zhang, Shurui Zhou, Alison Olechowski |
CHI | 2 |
| 2024 | Spatially embedded transformer: A point cloud deep learning model for aero-engine coaxiality prediction based on virtual measurement
Xin Jin 0022, Zhijing Zhang, Chaojiang Li, Steven Wang |
Adv. Eng. Informatics | 4 |
| 2022 | A hierarchical assembly knowledge representation framework and microdevice assembly ontology
Dongsheng Zhu, Zhijing Zhang, Lingling Shi, Jiahui Qian, Saren Qimuge |
Adv. Eng. Informatics | 2 |
| 2009 | Coupling Analysis of Manufacturing Characteristics and Mechanics Property of Microminiature Gear Mechanism Based on Neural Network
Xin Jin 0022, Zhijing Zhang, Fuchang Zuo, Zhongxin Li |
ISNN (1) | 2 |