Demo Data from EDBT 2026 / reviewers in the wild / expert
Oliver Ye
dblp:337/1778
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Computer networks · 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 |
Embedded and real-time systems · 50% Distributed systems · 38% Energy-efficient computing · 12% |
Topics — the 1 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Embedded and real-time systems
brain-computer interface |
0.7 | 1 | 2023 | SCALO: An Accelerator-Rich Distributed System for Scalable Brain-Computer Interfacing · ISCA 2023 |
Methods — techniques the papers use, named apart from their topics
cross-layer hardware-software co-design · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Towards Accessible Model-Free VerificationabstractDespite coming up on two decades of network verification research, verification tooling continues to see limited real-world adoption and outages continue to occur. Relying on interviews with network engineers and our own experience as a large network operator, we ask why. These conversations reveal that the culprit is traditional verification's reliance on hand-crafted network models, which leads to issues with coverage, correctness, maintainability, and fidelity, ultimately hindering practical applicability and adoption. Alexander Krentsel, Oliver Ye, Anthony Tafoya, Xuqian Ma, Sylvia Ratnasamy, Anees Shaikh |
HotNets | 2 |
| 2023 | SCALO: An Accelerator-Rich Distributed System for Scalable Brain-Computer InterfacingabstractSCALO is the first distributed brain-computer interface (BCI) consisting of multiple wireless-networked implants placed on different brain regions. SCALO unlocks new treatment options for debilitating neurological disorders and new research into brain-wide network behavior. Achieving the fast and low-power communication necessary for real-time processing has historically restricted BCIs to single brain sites. SCALO also adheres to tight power constraints, but enables fast distributed processing. Central to SCALO's efficiency is its realization as a full stack distributed system of brain implants with accelerator-rich compute. SCALO balances modular system layering with aggressive cross-layer hardware-software co-design to integrate compute, networking, and storage. The result is a lesson in designing energy-efficient networked distributed systems with hardware accelerators from the ground up. Karthik Sriram, Raghavendra Pradyumna Pothukuchi, Michal Gerasimiuk, Muhammed Ugur, Oliver Ye, Rajit Manohar, Anurag Khandelwal, Abhishek Bhattacharjee |
ISCA | 5 |