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Researcher · Hebrew University of Jerusalem · Israel

Yoram Singer

Yoram Singer publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics, on topics such as Machine Learning and Algorithms, Algorithms and Data Compression and Sparse and Compressive Sensing Techniques.

OpenAlex
World rank
#39,274
of 7,710,434 ranked researchers
Rank in Israel
#368
of 38,292
Works
180
Citations
43k
Citations per work
238.2
h-index
62
i10-index 129Top 0.5% in Artificial Intelligence
Yoram Singer

What does Yoram Singer research?

Yoram Singer's work is mostly in Computer Science (62%), Engineering (5%), Decision Sciences (5%).

Physical Sciences: 121 works · 67.4% of their workComputer Science: 112 works · 62.0% of their workArtificial Intelligence: 87 works · 48.1% of their workComputer Vision and Pattern Recognition: 19 works · 10.8% of their workSignal Processing: 6 works · 3.1% of their workEngineering: 10 works · 5.4% of their workComputational Mechanics: 10 works · 5.4% of their workEverything else: 44 works · 24.7% of their workSocial Sciences: 9 works · 4.8% of their workDecision Sciences: 9 works · 4.8% of their workManagement Science and Operations Research: 9 works · 4.8% of their workHealth Sciences: 6 works · 3.1% of their workMedicine: 6 works · 3.1% of their workPublic Health, Environmental and Occupational Health: 6 works · 3.1% of their workTHEIR WORKYoram Singer180works
Physical Sciences67%Health Sciences3%Social Sciences5%

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 Science11262.0%
    2. Engineering105.4%
  2. Everything else4424.7%
    1. Decision Sciences94.8%
    1. Medicine63.1%
  1. 1Artificial Intelligence48% of their works
  2. 2Computer Vision and Pattern Recognition11% of their works
  3. 3Computational Mechanics5% of their works
  4. 4Management Science and Operations Research5% of their works
  5. 5Signal Processing3% of their works
  6. 6Public Health, Environmental and Occupational Health3% of their works

Research topics

Which keywords describe Yoram Singer's research?

The keywords of their largest research topics: Deep Learning, Convex Optimization, Active Learning, Machine Learning, Sparse Representation, Feature Selection, Compressed Sensing and Compression.

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.

All 20 words, with their numbers
  1. Deep Learning832 topics
  2. Convex Optimization723 topics
  3. Active Learning591 topic
  4. Machine Learning591 topic
  5. Sparse Representation422 topics
  6. Feature Selection342 topics
  7. Compressed Sensing271 topic
  8. Compression271 topic
  9. Signal Recovery271 topic
  10. Suffix Arrays271 topic
  11. Random Projections241 topic
  12. Stochastic Gradient Descent241 topic
  13. Bandit Optimization211 topic
  14. Bayesian Optimization211 topic
  15. Multi-label Learning191 topic
  16. Text Classification191 topic
  17. Dimensionality Reduction151 topic
  18. Face Recognition151 topic
  19. Neural Machine Translation141 topic
  20. Statistical Machine Translation141 topic

Where does Yoram Singer work?

Yoram Singer's main affiliation in the publication record is Hebrew University of Jerusalem, Israel.

How many publications and citations does Yoram Singer have?

OpenAlex counts 180 works and 42,880 citations for Yoram Singer, an h-index of 62, ranking #39,274 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised 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.

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