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Researcher · Nvidia (United States) · United States

Tom Gibbs

Tom Gibbs publishes mostly in Molecular Biology, Information Systems and Artificial Intelligence, on topics such as Scientific Computing and Data Management, Computational Drug Discovery Methods and Topic Modeling.

ORCID 0000-0002-9196-5830 OpenAlex
World rank
#1,097,513
of 7,710,434 ranked researchers
Rank in United States
#355,197
of 1,627,098
Works
29
Citations
3.3k
Citations per work
112.3
h-index
12
i10-index 17Top 65% in Molecular Biology
Tom Gibbs

What does Tom Gibbs research?

Tom Gibbs's work is mostly in Computer Science (26%), Biochemistry, Genetics and Molecular Biology (13%), Physics and Astronomy (6%).

Everything else: 14 works · 48.8% of their workPhysical Sciences: 9 works · 32.0% of their workComputer Science: 7 works · 25.6% of their workInformation Systems: 3 works · 11.5% of their workArtificial Intelligence: 2 works · 7.7% of their workComputer Networks and Communications: 2 works · 6.4% of their workPhysics and Astronomy: 2 works · 6.4% of their workNuclear and High Energy Physics: 2 works · 6.4% of their workLife Sciences: 4 works · 12.8% of their workBiochemistry, Genetics and Molecular Biology: 4 works · 12.8% of their workMolecular Biology: 4 works · 12.8% of their workSocial Sciences: 2 works · 6.4% of their workDecision Sciences: 2 works · 6.4% of their workInformation Systems and Management: 2 works · 6.4% of their workTHEIR WORKTom Gibbs29works
Life Sciences13%Physical Sciences32%Social Sciences6%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
  1. Everything else1448.8%
    1. Computer Science725.6%
    2. Physics and Astronomy26.4%
    1. Biochemistry, Genetics and Molecular Biology412.8%
    1. Decision Sciences26.4%
  1. 1Molecular Biology13% of their works
  2. 2Information Systems12% of their works
  3. 3Artificial Intelligence8% of their works
  4. 4Nuclear and High Energy Physics6% of their works
  5. 5Information Systems and Management6% of their works
  6. 6Computer Networks and Communications6% of their works

Research topics

Which keywords describe Tom Gibbs's research?

The keywords of their largest research topics: Machine Learning, Reproducibility, Scientific Workflows, Molecular Docking, Virtual Screening, COVID-19, Distributed Storage and High-Throughput.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 17 words, with their numbers
  1. Machine Learning93 topics
  2. Reproducibility51 topic
  3. Scientific Workflows51 topic
  4. Molecular Docking41 topic
  5. Virtual Screening41 topic
  6. COVID-1931 topic
  7. Distributed Storage31 topic
  8. High-Throughput31 topic
  9. Materials Informatics31 topic
  10. Modeling31 topic
  11. Network Coding31 topic
  12. Neural Networks31 topic
  13. Word Representation31 topic
  14. Bioimage Analysis21 topic
  15. High-Content Screening21 topic
  16. Single-Cell21 topic
  17. Transcriptomics21 topic

Where does Tom Gibbs work?

Tom Gibbs's main affiliation in the publication record is Nvidia (United States), United States.

How many publications and citations does Tom Gibbs have?

OpenAlex counts 29 works and 3,258 citations for Tom Gibbs, an h-index of 12, ranking #1,097,513 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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