VLDB 2026 Research / reviewers in the wild / expert
Ridwan Jalali
dblp:322/5288
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 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 |
Graph learning · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Graph learning
graph network simulators |
0.6 | 1 | 2022 | Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator · KDD 2022 |
Machine learning › Graph learning
graph neural network |
0.6 | 1 | 2022 | Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator · KDD 2022 |
Methods — techniques the papers use, named apart from their topics
surrogate modeling · 1.1hybrid graph network · 1.1finite difference simulation · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Learning Large-scale Subsurface Simulations with a Hybrid Graph Network SimulatorabstractSubsurface simulations use computational models to predict the flow of fluids (e.g., oil, water, gas) through porous media. These simulations are pivotal in industrial applications such as petroleum production, where fast and accurate models are needed for high-stake decision making, for example, for well placement optimization and field development planning. Classical finite difference numerical simulators require massive computational resources to model large-scale real-world reservoirs. Alternatively, streamline simulators and data-driven surrogate models are computationally more efficient by relying on approximate physics models, however they are insufficient to model complex reservoir dynamics at scale. Tailin Wu, Qinchen Wang, Rex Ying, Kaidi Cao, Rok Sosic, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec |
KDD | 7 |