VLDB 2026 Research / reviewers in the wild / expert
Huang Luo
dblp:360/7517
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution
build systems |
0.7 | 1 | 2023 | Predicting Compilation Resources for Adaptive Build in an Industrial Setting · ASE 2023 |
Cloud and datacenter computing
cluster resource management and scheduling |
0.2 | 1 | 2023 | Predicting Compilation Resources for Adaptive Build in an Industrial Setting · ASE 2023 |
Methods — techniques the papers use, named apart from their topics
machine learning prediction · 1.3linear regression · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Learning Distinct Semantic Information in Modality-Specific and Modality-Shared Features for Visible-Infrared Person Re-identification
Min Jiang 0008, Huang Luo, Jun Kong 0001, Ming Lu 0008 |
PRCV (17) | 2 |
| 2023 | Predicting Compilation Resources for Adaptive Build in an Industrial SettingabstractDevelopment teams in large companies often maintain a huge codebase whose build time can be painfully long in a single machine. To reduce the build time, tools such as Bazel and distcc are used to build the code base in a distributed way. However, in the process of distributed build, certain remote slave machines can crash due to two types of errors: Out Of Memory (OOM) and Deadline Exceeded (DE) errors. These crashes lead to time-consuming rebuilds, as suffered by the WeiXin Group (WXG) of Tencent Inc. (the vendor of WeChat, a highly popular mobile app in China). Aiming to prevent these two types of errors, in this paper, we propose a new approach named PCRLINEAR, which predicts the memory and time requirements of the given C++ file, allowing the underlying distributed build system to schedule compilation resources adaptively according to the prediction results. Our experiments show that PCRLINEAR reduces the number of OOM and DE errors from 5% to 0.2% and, at the same time, achieves substantial build-performance improvement of 30% on average. Chaozheng Wang, Hailiang Huang 0004, Huang Luo, Yuetang Deng, Tao Xie 0001 |
ASE | 4 |