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Indranil Gupta

20 papers hereh-index 457.4k citations192 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author8
  • last author11

Across the 19 of 20 papers where every author was matched, so the position is known.

fields
  • cs.DC11
  • cs.LG6
  • cs.DB2
  • cs.NI1
same name
  • Indranil Gupta — 3 papers, h 2
  • Indranil Gupta — 2 papers, h 2
  • Indranil Gupta — 2 papers, h 1
  • Indranil Gupta — 2 papers, h 1
  • Indranil Gupta — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20052023
most citedAsynchronous Federated Optimization

434 citations · 851 across the 20 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.DC2023★ 1 cited

Dirigo: Self-scaling Stateful Actors For Serverless Real-time Data Processing

Le Xu, Divyanshu Saxena, Neeraja J. Yadwadkar +2

We propose Dirigo, a distributed stream processing service built atop virtual actors. Dirigo achieves both a high level of resource efficiency and performance isolation driven by u…

cs.DC2023

CoMesh: Fully-Decentralized Control for Sense-Trigger-Actuate Routines in Edge Meshes

Anna Karanika, Rui Yang, Xiaojuan Ma +3

While mesh networking for edge settings (e.g., smart buildings, farms, battlefields, etc.) has received much attention, the layer of control over such meshes remains largely centra…

cs.DB2023

Transactional Panorama: A Conceptual Framework for User Perception in Analytical Visual Interfaces

Dixin Tang, Alan Fekete, Indranil Gupta +1

Many tools empower analysts and data scientists to consume analysis results in a visual interface, such as a dashboard. When the underlying data changes, these results need to be u…

cs.DC2023★ 1 cited

Baechi: Fast Device Placement of Machine Learning Graphs

Beomyeol Jeon, Linda Cai, Chirag Shetty +6

Machine Learning graphs (or models) can be challenging or impossible to train when either devices have limited memory, or models are large. To split the model across devices, learn…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.