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S. Biswas

4 papers hereh-index 9377 citations18 works total

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

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • astro-ph.EP1
  • eess.SY1
  • math.OC1
  • stat.ML1
same name
  • S. Biswas — 134 papers
  • S. Biswas — 27 papers, h 56
  • S. Biswas — 24 papers, h 10
  • S. Biswas — 23 papers, h 13
  • S. Biswas — 19 papers, h 7
  • S. Biswas — 17 papers, h 10

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
20162020
most citedA Search for Technosignatures Around 31 Sun-like Stars with the Green Bank Telescope at 1.15-1.73 GHz

36 citations · 46 across the 3 of their papers we have counts for

collaborators

4 papers

astro-ph.EP2020★ 36 cited

A Search for Technosignatures Around 31 Sun-like Stars with the Green Bank Telescope at 1.15-1.73 GHz

Jean-Luc Margot, Pavlo Pinchuk, Robert Geil +37

We conducted a search for technosignatures in April of 2018 and 2019 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused…

eess.SY2019

Quantifying Robotic Swarm Coverage

Brendon G. Anderson, Eva Loeser, Marissa Gee +5

In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation…

math.OC2018★ 7 cited

Quantitative Assessment of Robotic Swarm Coverage

Brendon G. Anderson, Eva Loeser, Marissa Gee +5

This paper studies a generally applicable, sensitive, and intuitive error metric for the assessment of robotic swarm density controller performance. Inspired by vortex blob numeric…

stat.ML2016★ 3 cited

Towards Wide Learning: Experiments in Healthcare

Snehasis Banerjee, Tanushyam Chattopadhyay, Swagata Biswas +4

In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline. Feature engineering is wide…

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