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S. Deepak Narayanan

dblp:256/8113 · DBLP profile ↗
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2ranked-venue papers
2as first author
1since 2021 · last 2022
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Computer networks · 1 · 1 first-author

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.

Artificial intelligence
1 paper
Graph learning · 50% Efficient and distributed learning · 50%
Computer networks
1 paper
Internet of things and sensor networks · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Environmental and earth informatics · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
efficient training
0.612022
IGLU: Efficient GCN Training via Lazy Updates · ICLR 2022
Machine learning › Graph learning › graph neural network training
graph convolutional network training
0.612022
IGLU: Efficient GCN Training via Lazy Updates · ICLR 2022
Internet of things and sensor networks
sensor placement
0.412020
A toolkit for spatial interpolation and sensor placement: poster abstract · SenSys 2020
Internet of things and sensor networks
spatial interpolation
0.412020
A toolkit for spatial interpolation and sensor placement: poster abstract · SenSys 2020
Environmental and earth informatics
environmental sensing
0.112020
A toolkit for spatial interpolation and sensor placement: poster abstract · SenSys 2020

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

spatial interpolation algorithms · 0.9
YearPublicationVenuePosition
2022 IGLU: Efficient GCN Training via Lazy Updates
S. Deepak Narayanan, Aditya Sinha, Prateek Jain 0002, Purushottam Kar, Sundararajan Sellamanickam
ICLR1
2020 A toolkit for spatial interpolation and sensor placement: poster abstract
abstract
Sensing is central to the SenSys and related communities. However, fine-grained spatial sensing remains a challenge despite recent advancements, owing to cost, maintenance, among other factors. Thus, estimating the sensed phenomenon at unmonitored locations and strategically installing sensors is of prime importance. In this work, we introduce Polire - an open-source tool that provides a suite of algorithms for spatial interpolation and near-field passive sensor placements. We replicate two existing papers on these two tasks to show the efficacy of Polire. We believe that Polire is an essential step towards lowering entry barriers towards sensing and scientific reproducibility.
S. Deepak Narayanan, Zeel B. Patel, Apoorv Agnihotri, Nipun Batra 0001
SenSys1