Yoni Halpern

17 papers receiving 912 citations

Peers

Yoni Halpern
Comparison fields: 5 of 128
  • Health Informatics 59
  • Health Information Management 105
  • Family Practice 21
  • Artificial Intelligence 439
  • Safety Research 66
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Yacine Jernite United States
Jason Fries United States
Edmon Begoli United States
Matthew B. A. McDermott United States
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Hercules Dalianis Sweden
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Citations per field
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Citations per year

Countries citing papers authored by Yoni Halpern

Since Specialization
Citations

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

Fields of papers citing papers by Yoni Halpern

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Yoni Halpern, 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 Yoni Halpern Line = papers co-authored together Yoni Halpern links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1 2017273
2 2017223
3 2016102
4 202097
5 201179
6
Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network.
201750
7
Using Anchors to Estimate Clinical State without Labeled Data.
201432
8 201927
9 201917
10 20189
11
Unsupervised learning of noisy-or Bayesian networks
20138
12 20198
13 20166
14 20173
15
Text Embeddings Contain Bias. Here's Why That Matters.
20183
16 20193
17
Benefits of Overparameterization in Single-Layer Latent Variable Generative Models.
20191

About Yoni Halpern

Yoni Halpern is a scholar working on Artificial Intelligence, Molecular Biology, Health Information Management, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 17 papers that have together received 941 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (4 papers), Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers), Biomedical Text Mining and Ontologies (3 papers), Digital Imaging for Blood Diseases (1 paper), Auction Theory and Applications (1 paper), Electronic Health Records Systems (1 paper) and Statistical Methods and Bayesian Inference (1 paper). The work is most often cited by research in Health Informatics (59 citations), Health Information Management (105 citations), Family Practice (21 citations), Artificial Intelligence (439 citations) and Safety Research (66 citations). Yoni Halpern has collaborated with scholars based in United States, Russia and Canada. Frequent co-authors include David Sontag, Steven Horng, Abdulhakim Tlimat, Larry Nathanson, Yacine Jernite, Nathan I. Shapiro, Youngduck Choi, Timothy D. Barfoot, D. Sculley and Pallavi Baljekar. Their work appears in journals such as Journal of the American Medical Informatics Association, International Journal of Medical Informatics, The International Journal of Robotics Research, Scientific Reports and Communications of the ACM.

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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