Science Explorer Back to mapMap
Discussion
Researcher · ARM (United Kingdom) · United Kingdom

Paul N. Whatmough

Paul N. Whatmough publishes mostly in Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence, on topics such as Advanced Neural Network Applications, Advanced Memory and Neural Computing and Low-power high-performance VLSI design.

ORCID 0000-0001-6222-4286 OpenAlex
World rank
#1,047,099
of 7,710,434 ranked researchers
Rank in United Kingdom
#84,433
of 367,258
Works
63
Citations
1.8k
Citations per work
28.1
h-index
19
i10-index 24Top 20% in Electrical and Electronic Engineering
Paul N. Whatmough

What does Paul N. Whatmough research?

Paul N. Whatmough's work is mostly in Computer Science (48%), Engineering (40%).

Physical Sciences: 55 works · 87.1% of their workComputer Science: 30 works · 47.6% of their workComputer Vision and Pattern Recognition: 14 works · 22.2% of their workArtificial Intelligence: 7 works · 11.9% of their workHardware and Architecture: 6 works · 9.7% of their workSignal Processing: 2 works · 3.8% of their workEngineering: 25 works · 39.5% of their workElectrical and Electronic Engineering: 22 works · 35.7% of their workBiomedical Engineering: 2 works · 3.8% of their workEverything else: 8 works · 12.9% of their workTHEIR WORKPaul N. Whatmough63works
Physical Sciences87%

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. Computer Science3047.6%
    2. Engineering2539.5%
  2. Everything else812.9%
  1. 1Electrical and Electronic Engineering36% of their works
  2. 2Computer Vision and Pattern Recognition22% of their works
  3. 3Artificial Intelligence12% of their works
  4. 4Hardware and Architecture10% of their works
  5. 5Biomedical Engineering4% of their works
  6. 6Signal Processing4% of their works

Research topics

Which keywords describe Paul N. Whatmough's research?

The keywords of their largest research topics: Deep Learning, Convolutional Neural Networks, Image Recognition, Neuromorphic Computing, Low-Power, Memristor, Resistive Switching and Approximate Computing.

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 19 words, with their numbers
  1. Deep Learning302 topics
  2. Convolutional Neural Networks251 topic
  3. Image Recognition251 topic
  4. Neuromorphic Computing242 topics
  5. Low-Power192 topics
  6. Memristor151 topic
  7. Resistive Switching151 topic
  8. Approximate Computing121 topic
  9. VLSI Circuits121 topic
  10. CMOS Image Sensors101 topic
  11. High-Speed Imaging101 topic
  12. Parallel Computing101 topic
  13. Performance Optimization101 topic
  14. Ferroelectricity91 topic
  15. Hafnium Oxide91 topic
  16. CMOS71 topic
  17. Neural Recording71 topic
  18. Feature Matching51 topic
  19. Local Descriptors51 topic

Where does Paul N. Whatmough work?

Paul N. Whatmough's main affiliation in the publication record is ARM (United Kingdom), United Kingdom.

How many publications and citations does Paul N. Whatmough have?

OpenAlex counts 63 works and 1,773 citations for Paul N. Whatmough, an h-index of 19, ranking #1,047,099 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.

Other researchers named Paul N. Whatmough

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

Ask a question or share a note about Paul N. Whatmough. Keep it to the research: no personal attacks, advertising or links. Any reader can report a post, which collapses it for everyone until it is opened.