EDBT 2026 Demo / reviewers in the wild / expert
Valentin Andrei
dblp:50/8920
· DBLP profile ↗
4ranked-venue papers
3as first author
1since 2021 · last 2025
0000-0001-7975-6879ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 3 first-authorSystems, architecture and hardware · 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
1 paper |
Performance modeling and evaluation · 61% Cloud and datacenter computing · 30% Processor architecture and microarchitecture · 9% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation
benchmarking |
0.9 | 1 | 2025 | DCPerf: An Open-Source, Battle-Tested Performance Benchmark Suite for Datacenter Workloads · ISCA 2025 |
Cloud and datacenter computing
datacenter workloads |
0.9 | 1 | 2025 | DCPerf: An Open-Source, Battle-Tested Performance Benchmark Suite for Datacenter Workloads · ISCA 2025 |
Performance modeling and evaluation
performance prediction |
0.9 | 1 | 2025 | DCPerf: An Open-Source, Battle-Tested Performance Benchmark Suite for Datacenter Workloads · ISCA 2025 |
Methods — techniques the papers use, named apart from their topics
workload characterization · 0.9performance benchmarking · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | DCPerf: An Open-Source, Battle-Tested Performance Benchmark Suite for Datacenter WorkloadsabstractWe present DCPerf, the first open-source performance benchmark suite actively used to inform procurement decisions for millions of CPU in hyperscale datacenters.Although numerous benchmarks exist, our evaluation reveals that they inaccurately project server performance for datacenter workloads or fail to scale to resemble production workloads on modern many-core servers.DCPerf distinguishes itself in two aspects: (1) it faithfully models essential software architectures and features of datacenter applications, such as microservice architecture and highly optimized multi-process or multi-thread concurrency; and (2) it strives to align its performance characteristics with those of production workloads, at both the system level and microarchitecture level.Both are made possible by our direct access to the source code and hyperscale production deployments of datacenter workloads.Additionally, we share real-world examples of using DCPerf in critical decision-making, such as selecting future CPU SKUs and guiding CPU vendors in optimizing their designs.Our evaluation demonstrates that DCPerf accurately projects the performance of representative production workloads within a 3.3% error margin across four generations of production servers introduced over a span of six years, with core counts varying widely from 36 to 176. Wei Su 0005, Abhishek Dhanotia, Jayneel Gandhi, Neha Gholkar, Shobhit O. Kanaujia, Maxim Naumov, Kalyan Subramanian, Valentin Andrei, Chunqiang Tang |
ISCA | 9 |
| 2017 | Detecting Overlapped Speech on Short Timeframes Using Deep Learning
Valentin Andrei, Horia Cucu, Corneliu Burileanu |
INTERSPEECH | 1 |
| 2015 | Counting competing speakers in a timeframe - human versus computer
Valentin Andrei, Horia Cucu, Andi Buzo, Corneliu Burileanu |
INTERSPEECH | 1 |
| 2014 | Detecting the number of competing speakers - human selective hearing versus spectrogram distance based estimator
Valentin Andrei, Horia Cucu, Andi Buzo, Corneliu Burileanu |
INTERSPEECH | 1 |