Samreen Anjum

4.9k total citations
9 papers, 202 citations indexed

About

Samreen Anjum is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Science Applications. According to data from OpenAlex, Samreen Anjum has authored 9 papers receiving a total of 202 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 2 papers in Computer Science Applications. Recurrent topics in Samreen Anjum's work include Domain Adaptation and Few-Shot Learning (4 papers), Multimodal Machine Learning Applications (3 papers) and Cancer Genomics and Diagnostics (2 papers). Samreen Anjum is often cited by papers focused on Domain Adaptation and Few-Shot Learning (4 papers), Multimodal Machine Learning Applications (3 papers) and Cancer Genomics and Diagnostics (2 papers). Samreen Anjum collaborates with scholars based in United States, United Kingdom and Italy. Samreen Anjum's co-authors include Danna Gurari, Chongyan Chen, Ines Simeone, Luigi Cerulo, Younes Mokrab, Michele Ceccarelli, Giuseppe Curigliano, Lucia Gemma Delogu, Barbara Seliger and Lance D. Miller and has published in prestigious journals such as BMC Bioinformatics, OncoImmunology and Proceedings of the ACM on Human-Computer Interaction.

In The Last Decade

Samreen Anjum

9 papers receiving 197 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Samreen Anjum United States	5	101	56	47	43	32	9	202
Nicolas Brieu Germany	11	121 1.2×	34 0.6×	28 0.6×	45 1.0×	38 1.2×	27	273
Yitao Tian China	6	156 1.5×	25 0.4×	35 0.7×	26 0.6×	68 2.1×	11	351
Xiangwei Ge China	8	106 1.0×	27 0.5×	100 2.1×	100 2.3×	75 2.3×	23	403
Yves‐Rémi Van Eycke Belgium	8	71 0.7×	14 0.3×	39 0.8×	25 0.6×	42 1.3×	12	200
Arvind Rao United States	9	162 1.6×	100 1.8×	9 0.2×	53 1.2×	78 2.4×	15	331
Mart van Rijthoven Netherlands	7	76 0.8×	33 0.6×	116 2.5×	21 0.5×	29 0.9×	8	338
Masaki Yamazaki Japan	9	49 0.5×	25 0.4×	21 0.4×	10 0.2×	43 1.3×	42	193
Tupitsyn Nn Russia	7	87 0.9×	50 0.9×	14 0.3×	21 0.5×	43 1.3×	74	195
Ksenija Kanjer Serbia	13	110 1.1×	13 0.2×	34 0.7×	45 1.0×	90 2.8×	32	336

Countries citing papers authored by Samreen Anjum

Since Specialization

Citations

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

Fields of papers citing papers by Samreen Anjum

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Samreen Anjum

This figure shows the co-authorship network connecting the top 25 collaborators of Samreen Anjum. A scholar is included among the top collaborators of Samreen Anjum 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 Samreen Anjum. Samreen Anjum 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:

9 of 9 papers shown

1.

Chen, Chongyan, Samreen Anjum, & Danna Gurari. (2023). VQA Therapy: Exploring Answer Differences by Visually Grounding Answers. 15269–15279. 3 indexed citations

2.

Chen, Chongyan, Samreen Anjum, & Danna Gurari. (2022). Grounding Answers for Visual Questions Asked by Visually Impaired People. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 19076–19085. 29 indexed citations

3.

Anjum, Samreen, et al.. (2022). VizWiz grand challenge workshop at CVPR 2022. ACM SIGACCESS Accessibility and Computing. 1–1. 4 indexed citations

4.

Anjum, Samreen, et al.. (2021). Exploring the Use of Deep Learning with Crowdsourcing to Annotate Images. 8(2). 76–106. 6 indexed citations

5.

Anjum, Samreen, Chi Lin, & Danna Gurari. (2021). CrowdMOT. Proceedings of the ACM on Human-Computer Interaction. 4(CSCW3). 1–25. 4 indexed citations

6.

Anjum, Samreen & Danna Gurari. (2020). CTMC: Cell Tracking with Mitosis Detection Dataset Challenge. 4228–4237. 18 indexed citations

7.

Anjum, Samreen, et al.. (2019). Dataset bias: A case study for visual question answering. Proceedings of the Association for Information Science and Technology. 56(1). 58–67. 7 indexed citations

8.

Hendrickx, Wouter, Ines Simeone, Samreen Anjum, et al.. (2017). Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis. OncoImmunology. 6(2). e1253654–e1253654. 128 indexed citations

9.

Anjum, Samreen, Sandro Morganella, Fulvio D’Angelo, Antonio Iavarone, & Michele Ceccarelli. (2015). VEGAWES: variational segmentation on whole exome sequencing for copy number detection. BMC Bioinformatics. 16(1). 315–315. 3 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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