Demo Data from VLDB 2026 / reviewers in the wild / expert
Yogesh Bansal
dblp:85/2526
· DBLP profile ↗
5ranked-venue papers
2as first author
5since 2021 · last 2024
0000-0002-0756-1455ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021Computer networks · 2 · 2 since 2021Applied, interdisciplinary, general and emerging 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.
| Computer networks
1 paper |
Network optimization and economics · 56% Edge and fog computing · 44% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Machine learning and data management · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing
cluster resource management and scheduling |
0.7 | 1 | 2023 | SelfTune: Tuning Cluster Managers · NSDI 2023 |
Edge and fog computing › resource management › resource provisioning
network provisioning |
0.6 | 1 | 2022 | Optimizing Network Provisioning through Cooperation · NSDI 2022 |
Network optimization and economics
resource allocation |
0.6 | 1 | 2022 | Optimizing Network Provisioning through Cooperation · NSDI 2022 |
Machine learning and data management › learned database components
learned configuration tuning |
0.2 | 1 | 2023 | SelfTune: Tuning Cluster Managers · NSDI 2023 |
Methods — techniques the papers use, named apart from their topics
machine learning · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Correction to: A neural meta-model for predicting winter wheat crop yield
Yogesh Bansal, David Lillis, M. Tahar Kechadi |
Mach. Learn. | 1 |
| 2024 | A neural meta model for predicting winter wheat crop yield
Yogesh Bansal, David Lillis, M. Tahar Kechadi |
Mach. Learn. | 1 |
| 2023 | SelfTune: Tuning Cluster Managers
Ajaykrishna Karthikeyan, Nagarajan Natarajan, Gagan Somashekar, Ranjita Bhagwan, Rodrigo Fonseca, Tatiana Racheva, Yogesh Bansal |
NSDI | 8 |
| 2022 | Optimizing Network Provisioning through Cooperation
Harsha Sharma, Parth Thakkar, Sagar Bharadwaj, Ranjita Bhagwan, Venkat N. Padmanabhan, Yogesh Bansal, P. Vijay Kumar, Kathleen Voelbel |
NSDI | 6 |
| 2022 | Towards a systematic understanding of blockchain governance in proposal voting: A dash case studyabstractThe transparent and immutable nature of blockchain provides incentives for organizations wishing to create and implement an open, decentralized governance structure. As members exercise their voting rights, a fault-tolerant record accumulates on the blockchain that can be analyzed to diagnose and intercept potential threats to the governing body. To date, there has not been a systematic study of on-chain governance with respect to voting. In this paper, we provide an analysis of blockchain governance through a case study of the first cryptocurrency to adopt on-chain voting, Dash. Our analysis introduces the key characteristics of blockchain governance, steps through a data-driven exploration of Dash's on-chain voting system, and highlights exploitable attack vectors and vulnerabilities for the subversion of Dash's on-chain voting system via a novel network analysis methodology. We then conclude with guidelines for other organizations looking to implement similar blockchain governance solutions while maintaining integrity in their operations. Lawrence Mosley, Hieu Pham 0002, Xiaoshi Guo, Yogesh Bansal, Eric Hare, Nadia Antony |
Blockchain Res. Appl. | 4 |