Michael Hind

6.7k citations
68 papers · 3.6k · 1 hit paper · h-index 28

Impact in

Papers in

Michael Hind

66 papers receiving 3.4k citations

Michael Hind's Hit Papers

AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias 2019 · 510 citations
5100+2+4Years since publication100200300400500

Peers

Michael Hind
Comparison fields: 5 of 106
  • Hardware and Architecture 1.7k
  • Software 866
  • Health Informatics 136
  • Artificial Intelligence 2.0k
  • Computer Networks and Communications 1.2k
Replace Yuriy Brun with:
Yuriy Brun United States
Daniel Le Métayer France
Christian Collberg United States
Bryan Ford United States
Rishabh Singh United States
Diptikalyan Saha India
Dinghao Wu United States
Kathi Fisler United States
Edward F. Gehringer United States
Julia Rubin Canada
Michael Hind relative to Yuriy Brun United States Yuriy Brun's profile →
Citations per field
00.5×11.9×
Yuriy Brun · 1×
Citations per year

Countries citing papers authored by Michael Hind

Since Specialization
Citations

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

Fields of papers citing papers by Michael Hind

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Michael Hind, 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 Michael Hind Line = papers co-authored together Michael Hind 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
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
Hit paper breakdown →
2019510
2 2001449
3 2000236
4 1999220
5 2005195
6 1999168
7 2005147
8 2000146
9 2004118
10 1999108
11 200292
12 199582
13
Using hardware performance monitors to understand the behavior of java applications
200470
14 201960
15 201157
16 199856
17 200253
18 200653
19 200150
20 200747

About Michael Hind

Michael Hind is a scholar working on Hardware and Architecture, Artificial Intelligence, Computer Networks and Communications, Information Systems and Software, having authored 68 papers that have together received 3.6k indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (40 papers), Logic, programming, and type systems (21 papers), Software Testing and Debugging Techniques (15 papers), Cloud Computing and Resource Management (11 papers), Software System Performance and Reliability (8 papers), Distributed systems and fault tolerance (8 papers), Formal Methods in Verification (8 papers) and Security and Verification in Computing (7 papers). The work is most often cited by research in Hardware and Architecture (1.7k citations), Software (866 citations), Health Informatics (136 citations), Artificial Intelligence (2.0k citations) and Computer Networks and Communications (1.2k citations). Michael Hind has collaborated with scholars based in United States, Netherlands and Belgium. Frequent co-authors include David Grove, Matthew Arnold, Stephen J. Fink, Jong-Deok Choi, Peter F. Sweeney, Michael Burke, Amer Diwan, Paul Carini, Vivek Sarkar and Kush R. Varshney. Their work appears in journals such as ACM SIGPLAN Notices, IBM Journal of Research and Development, ACM Transactions on Programming Languages and Systems, Science of Computer Programming and Journal of Machine Learning Research.

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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