EDBT 2026 Demo / reviewers in the wild / expert
Neil Agarwal
dblp:232/0904
· DBLP profile ↗
5ranked-venue papers
3as first author
4since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 5 · 3 first-author · 4 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
3 papers |
Transport protocols and congestion control · 63% Edge and fog computing · 25% Content delivery and video streaming · 12% | |
| Artificial intelligence
1 paper |
Efficient and distributed learning · 100% | |
| Network and information security
1 paper |
Privacy and data protection · 100% | |
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% |
Topics — the 11 heaviest of 13, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Transport protocols and congestion control
rate control |
0.9 | 1 | 2025 | Mowgli: Passively Learned Rate Control for Real-Time Video · NSDI 2025 |
Transport protocols and congestion control › real-time communication
real-time video |
0.9 | 1 | 2025 | Mowgli: Passively Learned Rate Control for Real-Time Video · NSDI 2025 |
Machine learning › Efficient and distributed learning
model compression |
0.7 | 1 | 2023 | Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge · NSDI 2023 |
Machine learning › Efficient and distributed learning
model merging |
0.7 | 1 | 2023 | Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge · NSDI 2023 |
Multimedia analysis and retrieval
video content analysis |
0.7 | 1 | 2023 | Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics · NSDI 2023 |
Edge and fog computing
video analytics |
0.7 | 1 | 2023 | Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge · NSDI 2023 |
Privacy and data protection
privacy-preserving query processing |
0.6 | 1 | 2022 | Privid: Practical, Privacy-Preserving Video Analytics Queries · NSDI 2022 |
Privacy and data protection › privacy-preserving computation › privacy-preserving multimedia processing
privacy-preserving video analytics |
0.6 | 1 | 2022 | Privid: Practical, Privacy-Preserving Video Analytics Queries · NSDI 2022 |
Transport protocols and congestion control
delay-based congestion control |
0.4 | 1 | 2020 | Mind the delay: the adverse effects of delay-based TCP on HTTP · CoNEXT 2020 |
Content delivery and video streaming
web performance |
0.4 | 1 | 2020 | Mind the delay: the adverse effects of delay-based TCP on HTTP · CoNEXT 2020 |
Edge and fog computing › video analytics
real-time video analytics |
0.2 | 1 | 2023 | Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge · NSDI 2023 |
Methods — techniques the papers use, named apart from their topics
passive learning · 0.9congestion control detection · 0.4client-side measurement · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Mowgli: Passively Learned Rate Control for Real-Time Video
Neil Agarwal, Rui Pan 0003, Francis Y. Yan, Ravi Netravali |
NSDI | 1 |
| 2023 | Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics
Neil Agarwal, Ravi Netravali |
NSDI | 1 |
| 2023 | Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge
Arthi Padmanabhan, Neil Agarwal, Anand Padmanabha Iyer, Ganesh Ananthanarayanan, Yuanchao Shu, Nikolaos Karianakis, Guoqing Harry Xu, Ravi Netravali |
NSDI | 2 |
| 2022 | Privid: Practical, Privacy-Preserving Video Analytics Queries
Frank Cangialosi, Neil Agarwal, Venkat Arun, Junchen Jiang, Srinivas Narayana, Anand D. Sarwate, Ravi Netravali |
NSDI | 2 |
| 2020 | Mind the delay: the adverse effects of delay-based TCP on HTTPabstractThe last three decades have seen much evolution in web and network protocols: amongst them, a transition from HTTP/1.1 to HTTP/2 and a shift from loss-based to delay-based TCP congestion control algorithms. This paper argues that these two trends come at odds with one another, ultimately hurting web performance. Using a controlled synthetic study, we show how delay-based congestion control protocols (e.g., BBR and CUBIC + Hybrid Slow Start) result in the underestimation of the available congestion window in mobile networks, and how that dramatically hampers the effectiveness of HTTP/2. To quantify the impact of such finding in the current web, we evolve the web performance toolbox in two ways. First, we develop Igor, a client-side TCP congestion control detection tool that can differentiate between loss-based and delay-based algorithms by focusing on their behavior during slow start. Second, we develop a Chromium patch which allows fine-grained control on the HTTP version to be used per domain. Using these new web performance tools, we analyze over 300 real websites and find that 67% of sites relying solely on delay-based congestion control algorithms have better performance with HTTP/1.1. Neil Agarwal, Matteo Varvello, Andrius Aucinas, Fabián E. Bustamante, Ravi Netravali |
CoNEXT | 1 |