Sameep Mehta

88 papers receiving 1.7k citations

Sameep Mehta's Hit Papers

AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias 2019 · 566 citations
5660+2+4Years since publication100200300400500

Peers

Sameep Mehta
Comparison fields: 5 of 140
  • Health Informatics 112
  • Safety Research 347
  • Artificial Intelligence 652
  • Information Systems 400
  • Statistical and Nonlinear Physics 204
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Eirini Ntoutsi Germany
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Countries citing papers authored by Sameep Mehta

Since Specialization
Citations

This map shows the geographic impact of Sameep Mehta'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 Sameep Mehta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sameep Mehta more than expected).

Fields of papers citing papers by Sameep Mehta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sameep Mehta. 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 Sameep Mehta. The network helps show where Sameep Mehta may publish in the future.

Co-authors

The 25 scholars most cited alongside Sameep Mehta, 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 Sameep Mehta Line = papers co-authored together Sameep Mehta links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 97 papers — load more, or switch the sort, to bring in the rest.

#Work
1
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
Hit paper breakdown →
2019566
2 2020189
3 2010118
4 202169
5 200855
6 201754
7 201349
8 201247
9 200544
10 201540
11 201834
12 200828
13 201827
14 201927
15 201925
16 201323
17 201921
18 201720
19
Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies
201819
20 201519

About Sameep Mehta

Sameep Mehta is a scholar working on Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 97 papers that have together received 1.8k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (17 papers), Topic Modeling (10 papers), Data Quality and Management (10 papers), Opinion Dynamics and Social Influence (9 papers), Data Mining Algorithms and Applications (8 papers), Time Series Analysis and Forecasting (7 papers), Data Management and Algorithms (7 papers) and Advanced Text Analysis Techniques (7 papers). The work is most often cited by research in Health Informatics (112 citations), Safety Research (347 citations), Artificial Intelligence (652 citations), Information Systems (400 citations) and Statistical and Nonlinear Physics (204 citations). Sameep Mehta has collaborated with scholars based in India, United States and United Kingdom. Frequent co-authors include Amitabha Bagchi, Rudra Mohan Tripathy, Nitin Gupta, Karthikeyan Natesan Ramamurthy, Michael Hind, Aleksandra Mojsilović, Stephanie Houde, Rachel Bellamy, Kuntal Dey and Srinivasan Parthasarathy. Their work appears in journals such as IBM Journal of Research and Development, Lecture notes in computer science, IEEE Software, World Wide Web and IEEE Transactions on Human-Machine Systems.

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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