Uri Shalit

4.0k total citations
32 papers, 1.1k citations indexed

About

Uri Shalit is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistics and Probability. According to data from OpenAlex, Uri Shalit has authored 32 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 5 papers in Statistics and Probability. Recurrent topics in Uri Shalit's work include Machine Learning in Healthcare (6 papers), Bayesian Modeling and Causal Inference (5 papers) and Advanced Causal Inference Techniques (5 papers). Uri Shalit is often cited by papers focused on Machine Learning in Healthcare (6 papers), Bayesian Modeling and Causal Inference (5 papers) and Advanced Causal Inference Techniques (5 papers). Uri Shalit collaborates with scholars based in Israel, United States and United Kingdom. Uri Shalit's co-authors include Gal Chechik, Samy Bengio, Varun Sharma, Malka Gorfine, Hagai Rossman, Tomer Meir, Smadar Shilo, Eran Segal, Marc Scott and Vincent Dorie and has published in prestigious journals such as Nature Medicine, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Uri Shalit

30 papers receiving 1.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Uri Shalit Israel	14	314	281	195	146	116	32	1.1k
Yuexi Peng China	19	411 1.3×	216 0.8×	137 0.7×	452 3.1×	16 0.1×	41	1.7k
Essam A. Rashed Japan	20	114 0.4×	190 0.7×	95 0.5×	275 1.9×	42 0.4×	97	1.2k
Yair Goldberg Israel	17	57 0.2×	48 0.2×	1.0k 5.3×	348 2.4×	438 3.8×	69	1.8k
Francisco Sahli Costabal Chile	21	33 0.1×	126 0.4×	106 0.5×	332 2.3×	23 0.2×	44	1.6k
Mingyuan Zhou United States	22	641 2.0×	728 2.6×	27 0.1×	13 0.1×	29 0.3×	113	1.9k
Marloes H. Maathuis Switzerland	20	33 0.1×	832 3.0×	100 0.5×	71 0.5×	13 0.1×	48	2.0k
Chong You China	10	39 0.1×	125 0.4×	294 1.5×	364 2.5×	47 0.4×	28	788
Gabriel Goh United States	10	112 0.4×	257 0.9×	58 0.3×	134 0.9×	11 0.1×	17	564
Qing Xu United States	12	80 0.3×	427 1.5×	27 0.1×	39 0.3×	39 0.3×	47	1.1k

Countries citing papers authored by Uri Shalit

Since Specialization

Citations

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

Fields of papers citing papers by Uri Shalit

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Uri Shalit

This figure shows the co-authorship network connecting the top 25 collaborators of Uri Shalit. A scholar is included among the top collaborators of Uri Shalit based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Uri Shalit. Uri Shalit is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Srebnik, Naama, et al.. (2024). Evaluating the heterogeneous effect of extended culture to blastocyst transfer on the implantation outcome via causal inference in fresh ICSI cycles. Journal of Assisted Reproduction and Genetics. 41(3). 703–715. 1 indexed citations

Mazwi, Mjaye, et al.. (2023). iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside. PLoS Computational Biology. 19(9). e1010835–e1010835.

Amir, Ofra, et al.. (2022). Tell me something interesting: Clinical utility of machine learning prediction models in the ICU. Journal of Biomedical Informatics. 132. 104107–104107. 7 indexed citations

Somer, Jonathan, Yaron Bar‐Lavie, Arnona Ziv, et al.. (2021). Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study. Journal of the American Medical Informatics Association. 28(6). 1188–1196. 24 indexed citations

Twig, Gilad, et al.. (2021). Socioeconomic disparities and COVID-19 vaccination acceptance: a nationwide ecologic study. Clinical Microbiology and Infection. 27(10). 1502–1506. 48 indexed citations

Rossman, Hagai, Tomer Meir, Jonathan Somer, et al.. (2021). Hospital load and increased COVID-19 related mortality in Israel. Nature Communications. 12(1). 1904–1904. 59 indexed citations

Barda, Noam, Dan Riesel, Joseph Levy, et al.. (2020). Developing a COVID-19 mortality risk prediction model when individual-level data are not available. Nature Communications. 11(1). 4439–4439. 77 indexed citations

Atzmon, Yuval, Felix Kreuk, Uri Shalit, & Gal Chechik. (2020). A causal view of compositional zero-shot recognition. arXiv (Cornell University). 33. 1462–1473. 4 indexed citations

Jesson, Andrew, Sören Mindermann, Uri Shalit, & Yarin Gal. (2020). Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. arXiv (Cornell University). 33. 11637–11649. 3 indexed citations

10.

Dorie, Vincent, Jennifer Hill, Uri Shalit, Marc Scott, & Daniel Cervone. (2018). Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition.. Grantee Submission. 6 indexed citations

11.

Atzmon, Yuval, Uri Shalit, & Gal Chechik. (2015). Learning Sparse Metrics, One Feature at a Time. Neural Information Processing Systems. 30–48. 7 indexed citations

12.

Shalit, Uri & Gal Chechik. (2014). Coordinate-descent for learning orthogonal matrices through Givens rotations. International Conference on Machine Learning. 548–556. 9 indexed citations

13.

Shalit, Uri, Daphna Weinshall, & Gal Chechik. (2013). Modeling Musical Influence with Topic Models. International Conference on Machine Learning. 244–252. 14 indexed citations

14.

Shalit, Uri, et al.. (2013). FuncISH: learning a functional representation of neural ISH images. Bioinformatics. 29(13). i36–i43. 8 indexed citations

15.

Shalit, Uri, Daphna Weinshall, & Gal Chechik. (2012). Online learning in the embedded manifold of low-rank matrices. Journal of Machine Learning Research. 13(1). 429–458. 26 indexed citations

16.

Shalit, Uri, et al.. (2011). Descending Systems Translate Transient Cortical Commands into a Sustained Muscle Activation Signal. Cerebral Cortex. 22(8). 1904–1914. 44 indexed citations

17.

Shalit, Uri, Daphna Weinshall, & Gal Chechik. (2010). Online Learning in The Manifold of Low-Rank Matrices. Neural Information Processing Systems. 23. 2128–2136. 25 indexed citations

18.

Chechik, Gal, Uri Shalit, Varun Sharma, & Samy Bengio. (2009). An Online Algorithm for Large Scale Image Similarity Learning. neural information processing systems. 22. 306–314. 69 indexed citations

19.

Chechik, Gal, Varun Sharma, Uri Shalit, & Samy Bengio. (2009). Large Scale Online Learning of Image Similarity through Ranking. Journal of Machine Learning Research. 11(36). 11–14. 299 indexed citations

20.

Harel, Ran, Itay Asher, Oren Cohen, et al.. (2008). Computation in spinal circuitry: Lessons from behaving primates. Behavioural Brain Research. 194(2). 119–128. 13 indexed citations

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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