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Researcher · Magellan Health · United States

Lawrence Hsieh

Lawrence Hsieh publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, on topics such as Topic Modeling, Multimodal Machine Learning Applications and Domain Adaptation and Few-Shot Learning.

OpenAlex
World rank
#4,952,440
of 7,710,434 ranked researchers
Rank in United States
#1,288,234
of 1,627,098
Works
15
Citations
0
h-index
0
i10-index 0Top 100% in Artificial Intelligence
Lawrence Hsieh

What does Lawrence Hsieh research?

Lawrence Hsieh's work is mostly in Computer Science (80%), Engineering (7%), Medicine (7%).

Physical Sciences: 13 works · 86.8% of their workComputer Science: 12 works · 80.1% of their workArtificial Intelligence: 6 works · 37.8% of their workComputer Vision and Pattern Recognition: 5 works · 35.6% of their workInformation Systems: 1 works · 6.7% of their workEngineering: 1 works · 6.7% of their workBiomedical Engineering: 1 works · 6.7% of their workHealth Sciences: 1 works · 6.7% of their workMedicine: 1 works · 6.7% of their workOrthopedics and Sports Medicine: 1 works · 6.7% of their workSocial Sciences: 1 works · 4.4% of their workPsychology: 1 works · 4.4% of their workExperimental and Cognitive Psychology: 1 works · 4.4% of their workTHEIR WORKLawrence Hsieh15works
Physical Sciences87%Health Sciences7%Social Sciences4%

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 Science1280.1%
    2. Engineering16.7%
    1. Medicine16.7%
    1. Psychology14.4%
  1. 1Artificial Intelligence38% of their works
  2. 2Computer Vision and Pattern Recognition36% of their works
  3. 3Information Systems7% of their works
  4. 4Biomedical Engineering7% of their works
  5. 5Orthopedics and Sports Medicine7% of their works
  6. 6Experimental and Cognitive Psychology4% of their works

Research topics

Which keywords describe Lawrence Hsieh's research?

The keywords of their largest research topics: Neural Networks, Deep Learning, Information Retrieval, Representation Learning, Unsupervised Learning, Image Captioning, Machine Translation and Visual Question Answering.

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 21 words, with their numbers
  1. Neural Networks154 topics
  2. Deep Learning103 topics
  3. Information Retrieval82 topics
  4. Representation Learning62 topics
  5. Unsupervised Learning62 topics
  6. Image Captioning51 topic
  7. Machine Translation51 topic
  8. Visual Question Answering51 topic
  9. Word Representation51 topic
  10. Domain Adaptation31 topic
  11. Generative Adversarial Networks31 topic
  12. Image Synthesis31 topic
  13. Search Engines31 topic
  14. Transfer Learning31 topic
  15. User Behavior31 topic
  16. Adversarial Examples21 topic
  17. Agent-Based Modeling21 topic
  18. Multi-Agent Systems21 topic
  19. Neural Machine Translation21 topic
  20. Robustness21 topic
  21. Statistical Machine Translation21 topic

Where does Lawrence Hsieh work?

Lawrence Hsieh's main affiliation in the publication record is Magellan Health, United States.

How many publications and citations does Lawrence Hsieh have?

OpenAlex counts 15 works and 0 citations for Lawrence Hsieh, an h-index of 0, ranking #4,952,440 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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