Sumit Basu

2.8k citations
68 papers · 1.7k · h-index 22

Impact in

Papers in

Sumit Basu

62 papers receiving 1.6k citations

Peers

Sumit Basu
Comparison fields: 5 of 140
  • Computer Science Applications 270
  • Computer Vision and Pattern Recognition 695
  • Signal Processing 347
  • Human-Computer Interaction 124
  • Artificial Intelligence 659
Replace Arvind Satyanarayan with:
Arvind Satyanarayan United States
Dominik Moritz United States
S. Levialdi Italy
Kanit Wongsuphasawat United States
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Sebastian Egger Austria
Carsten Görg United States
Jörn Kohlhammer Germany
Hakan Ferhatosmanoğlu United States
Danai Koutra United States
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Citations per field
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Citations per year

Countries citing papers authored by Sumit Basu

Since Specialization
Citations

This map shows the geographic impact of Sumit Basu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sumit Basu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sumit Basu more than expected).

Fields of papers citing papers by Sumit Basu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sumit Basu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sumit Basu. The network helps show where Sumit Basu may publish in the future.

Co-authors

The 25 scholars most cited alongside Sumit Basu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sumit Basu Line = papers co-authored together Sumit Basu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 68 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Learning from the Wisdom of Crowds by Minimax Entropy
2012198
2 1996144
3 2013107
4 2008106
5 200283
6
Selective supervision: guiding supervised learning with decision-theoretic active learning
200782
7 201569
8 200469
9
Towards Measuring Human Interactions in Conversational Settings
200154
10
Learning Human Interactions with the Influence Model
200150
11
Modeling Conversational Dynamics as a Mixed-Memory Markov Process
200449
12 201348
13 200348
14 201445
15 201744
16 202037
17 200336
18
Mind the Gap: Learning to Choose Gaps for Question Generation
201235
19 200234
20 200233

About Sumit Basu

Sumit Basu is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Radiation and Pulmonary and Respiratory Medicine, having authored 68 papers that have together received 1.7k indexed citations. Recurring topics across this work include Speech and Audio Processing (15 papers), Music and Audio Processing (12 papers), Advanced Vision and Imaging (9 papers), Music Technology and Sound Studies (7 papers), Advanced Radiotherapy Techniques (6 papers), Machine Learning and Algorithms (5 papers), Radiation Therapy and Dosimetry (5 papers) and Topic Modeling (5 papers). The work is most often cited by research in Computer Science Applications (270 citations), Computer Vision and Pattern Recognition (695 citations), Signal Processing (347 citations), Human-Computer Interaction (124 citations) and Artificial Intelligence (659 citations). Sumit Basu has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Alex Pentland, Lucy Vanderwende, Irfan Essa, Tanzeem Choudhury, John Platt, Dengyong Zhou, Yi Mao, Dan Morris, Ian Simon and Brian Clarkson. Their work appears in journals such as Medical Physics, Scientific Reports, Classical and Quantum Gravity, Transactions of the Association for Computational Linguistics and Speech Communication.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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