Demo Data from VLDB 2026 / reviewers in the wild / expert
Luoxi Meng
dblp:342/1564
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2024
0009-0009-0645-834XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 1 since 2021Software engineering, systems software and programming languages · 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.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
Energy-efficient computing · 62% Cloud and datacenter computing · 38% | |
| Artificial intelligence
1 paper |
Efficient and distributed learning · 100% | |
| Network and information security
1 paper |
Authentication and access control · 100% |
Topics — the 3 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
distributed training |
0.8 | 1 | 2024 | Reducing Energy Bloat in Large Model Training · SOSP 2024 |
Authentication and access control
authorization |
0.8 | 1 | 2024 | Stateful Least Privilege Authorization for the Cloud · USENIX Security Symposium 2024 |
Cloud and datacenter computing
cloud security |
0.2 | 1 | 2024 | Stateful Least Privilege Authorization for the Cloud · USENIX Security Symposium 2024 |
Methods — techniques the papers use, named apart from their topics
energy profiling · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Reducing Energy Bloat in Large Model TrainingabstractTraining large AI models on numerous GPUs consumes a massive amount of energy, making power delivery one of the largest limiting factors in building and operating datacenters for AI workloads. However, we observe that not all energy consumed during training directly contributes to end-to-end throughput; a significant portion can be removed without slowing down training. We call this portion energy bloat. Jae-Won Chung, Yile Gu, Insu Jang, Luoxi Meng, Nikhil Bansal 0001, Mosharaf Chowdhury |
SOSP | 4 |
| 2024 | Stateful Least Privilege Authorization for the Cloud
Leo Cao, Luoxi Meng, Deian Stefan, Earlence Fernandes |
USENIX Security Symposium | 2 |