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Abhishek Mathur

dblp:45/2321 · DBLP profile ↗
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3ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 since 2021Software engineering, systems software and programming languages · 1

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
Distributed systems · 67% Cloud and datacenter computing · 33%
Computer graphics and multimedia
1 paper
Multimedia systems and quality of experience · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing
cluster resource management and scheduling
0.312017
SVE: Distributed Video Processing at Facebook Scale · SOSP 2017
Distributed systems › distributed data processing
distributed video processing
0.312017
SVE: Distributed Video Processing at Facebook Scale · SOSP 2017
Distributed systems
fault tolerance
0.312017
SVE: Distributed Video Processing at Facebook Scale · SOSP 2017

Methods — techniques the papers use, named apart from their topics

streaming architecture · 0.6
YearPublicationVenuePosition
2026 From 2D to 3D surgical instrument tracking: a technique based on intervals and geometric cues
Shubhangi Nema, Abhishek Mathur, Leena Vachhani
Mach. Vis. Appl.2
2017 SVE: Distributed Video Processing at Facebook Scale
abstract
Videos are an increasingly utilized part of the experience of the billions of people that use Facebook. These videos must be uploaded and processed before they can be shared and downloaded. Uploading and processing videos at our scale, and across our many applications, brings three key requirements: low latency to support interactive applications; a flexible programming model for application developers that is simple to program, enables efficient processing, and improves reliability; and robustness to faults and overload. This paper describes the evolution from our initial monolithic encoding script (MES) system to our current Streaming Video Engine (SVE) that overcomes each of the challenges. SVE has been in production since the fall of 2015, provides lower latency than MES, supports many diverse video applications, and has proven to be reliable despite faults and overload.
Petchean Ang, Peter Knowles, Tomasz Nykiel, Iaroslav Tverdokhlib, Amit Yajurvedi, Paul Dapolito IV, Xifan Yan, Maxim Bykov, Chuen Liang, Mohit Talwar, Abhishek Mathur, Sachin Kulkarni, Matthew Burke 0001, Wyatt Lloyd
SOSP12
2006 Using Windows Printer Drivers for Solaris Applications - An Application of Multiagent System
Wanli Ma 0003, Dat Tran 0001, Dharmendra Sharma 0001, Abhishek Mathur
KES (3)4