Uri Shaham

3.9k total citations · 1 hit paper
16 papers, 1.6k citations indexed

About

Uri Shaham is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Uri Shaham has authored 16 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 2 papers in Molecular Biology and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Uri Shaham's work include Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers) and Image and Signal Denoising Methods (2 papers). Uri Shaham is often cited by papers focused on Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers) and Image and Signal Denoising Methods (2 papers). Uri Shaham collaborates with scholars based in United States and Israel. Uri Shaham's co-authors include Alexander Cloninger, Yuval Kluger, Tingting Jiang, Jonathan Bates, Jared Katzman, Sahand Negahban, Yutaro Yamada, Ronald R. Coifman, Kelly Stanton and Huamin Li and has published in prestigious journals such as Bioinformatics, International Journal of Radiation Oncology*Biology*Physics and Pattern Recognition.

In The Last Decade

Uri Shaham

16 papers receiving 1.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

2018 973 citations Jared Katzman, Uri Shaham et al. BMC Medical Research Methodology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Uri Shaham United States	11	748	386	325	187	154	16	1.6k
Alexander Cloninger United States	9	529 0.7×	432 1.1×	152 0.5×	183 1.0×	81 0.5×	32	1.3k
Κωνσταντίνα Κούρου Greece	10	905 1.2×	570 1.5×	574 1.8×	240 1.3×	110 0.7×	31	2.3k
Mohamed Amgad United States	18	692 0.9×	717 1.9×	250 0.8×	156 0.8×	195 1.3×	32	1.6k
Konstantinos Exarchos Greece	11	880 1.2×	600 1.6×	619 1.9×	309 1.7×	112 0.7×	35	2.4k
Safoora Yousefi United States	5	480 0.6×	457 1.2×	174 0.5×	107 0.6×	97 0.6×	7	883
Thomas Ganslandt Germany	20	462 0.6×	282 0.7×	400 1.2×	110 0.6×	117 0.8×	109	1.8k
Christopher Ré United States	19	1.3k 1.7×	677 1.8×	398 1.2×	244 1.3×	237 1.5×	47	2.3k
Tobias Rydèn Sweden	20	654 0.9×	257 0.7×	261 0.8×	144 0.8×	50 0.3×	55	1.9k
Brandon D. Gallas United States	21	425 0.6×	839 2.2×	178 0.5×	517 2.8×	121 0.8×	83	1.7k
Mikhail Zaslavskiy France	10	578 0.8×	433 1.1×	302 0.9×	117 0.6×	271 1.8×	17	1.3k

Countries citing papers authored by Uri Shaham

Since Specialization

Citations

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

Fields of papers citing papers by Uri Shaham

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Uri Shaham

This figure shows the co-authorship network connecting the top 25 collaborators of Uri Shaham. A scholar is included among the top collaborators of Uri Shaham 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 Shaham. Uri Shaham is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

1.

Shaham, Uri, et al.. (2024). Multilingual Instruction Tuning With Just a Pinch of Multilinguality. 2304–2317. 3 indexed citations

2.

Shaham, Uri, Maha Elbayad, Vedanuj Goswami, Omer Levy, & Shruti Bhosale. (2023). Causes and Cures for Interference in Multilingual Translation. 15849–15863. 3 indexed citations

3.

Honovich, Or, Uri Shaham, Samuel R. Bowman, & Omer Levy. (2023). Instruction Induction: From Few Examples to Natural Language Task Descriptions. 1935–1952. 28 indexed citations

4.

Shaham, Uri, et al.. (2023). ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding. 7977–7989. 7 indexed citations

5.

Shaham, Uri, et al.. (2023). Efficient Long-Text Understanding with Short-Text Models. Transactions of the Association for Computational Linguistics. 11. 284–299. 18 indexed citations

6.

Shaham, Uri, et al.. (2022). Deep unsupervised feature selection by discarding nuisance and correlated features. Neural Networks. 152. 34–43. 15 indexed citations

7.

Shaham, Uri, Elad Segal, Avia Efrat, et al.. (2022). SCROLLS: Standardized CompaRison Over Long Language Sequences. 12007–12021. 27 indexed citations

8.

Efrat, Avia, et al.. (2021). Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 4186–4192. 4 indexed citations

9.

Katzman, Jared, Uri Shaham, Alexander Cloninger, et al.. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology. 18(1). 24–24. 973 indexed citations breakdown →

10.

Shaham, Uri, Yutaro Yamada, & Sahand Negahban. (2018). Understanding adversarial training: Increasing local stability of supervised models through robust optimization. Neurocomputing. 307. 195–204. 157 indexed citations

11.

Li, Huamin, Uri Shaham, Kelly Stanton, et al.. (2017). Gating mass cytometry data by deep learning. Bioinformatics. 33(21). 3423–3430. 58 indexed citations

12.

Shaham, Uri & Roy R. Lederman. (2017). Learning by coincidence: Siamese networks and common variable learning. Pattern Recognition. 74. 52–63. 23 indexed citations

13.

Aneja, Sanjay, Uri Shaham, Nathan Pirakitikulr, et al.. (2017). Deep Neural Network to Predict Local Failure Following Stereotactic Body Radiation Therapy: Integrating Imaging and Clinical Data to Predict Outcomes. International Journal of Radiation Oncology*Biology*Physics. 99(2). S47–S47. 5 indexed citations

14.

Shaham, Uri, Kelly Stanton, Jun Zhao, et al.. (2017). Removal of batch effects using distribution-matching residual networks. Bioinformatics. 33(16). 2539–2546. 110 indexed citations

15.

Mishne, Gal, Uri Shaham, Alexander Cloninger, & Israel Cohen. (2017). Diffusion nets. Applied and Computational Harmonic Analysis. 47(2). 259–285. 24 indexed citations

16.

Shaham, Uri, Alexander Cloninger, & Ronald R. Coifman. (2016). Provable approximation properties for deep neural networks. Applied and Computational Harmonic Analysis. 44(3). 537–557. 111 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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