Israt Nisa

508 total citations
16 papers, 302 citations indexed

About

Israt Nisa is a scholar working on Artificial Intelligence, Computer Networks and Communications and Hardware and Architecture. According to data from OpenAlex, Israt Nisa has authored 16 papers receiving a total of 302 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 7 papers in Computer Networks and Communications and 7 papers in Hardware and Architecture. Recurrent topics in Israt Nisa's work include Parallel Computing and Optimization Techniques (7 papers), Tensor decomposition and applications (5 papers) and Advanced Graph Neural Networks (5 papers). Israt Nisa is often cited by papers focused on Parallel Computing and Optimization Techniques (7 papers), Tensor decomposition and applications (5 papers) and Advanced Graph Neural Networks (5 papers). Israt Nisa collaborates with scholars based in United States and China. Israt Nisa's co-authors include Aravind Sukumaran-Rajam, P. Sadayappan, Changwan Hong, Jiajia Li, Richard Vuduc, Charles Siegel, Abhinav Vishnu, Ümit V. Çatalyürek, Aydın Buluç and Jinsung Kim and has published in prestigious journals such as Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

In The Last Decade

Israt Nisa

14 papers receiving 296 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Israt Nisa United States 9 189 124 124 109 81 16 302
David Lugato France 7 200 1.1× 112 0.9× 108 0.9× 65 0.6× 67 0.8× 15 325
Hongbo Rong United States 10 265 1.4× 163 1.3× 70 0.6× 42 0.4× 43 0.5× 29 317
Anand Venkat United States 10 178 0.9× 120 1.0× 60 0.5× 52 0.5× 19 0.2× 12 223
Benoît Meister United States 10 269 1.4× 191 1.5× 76 0.6× 13 0.1× 84 1.0× 29 344
Naser Sedaghati United States 9 311 1.6× 258 2.1× 79 0.6× 59 0.5× 15 0.2× 11 394
Benjamin Lipshitz United States 8 191 1.0× 175 1.4× 78 0.6× 33 0.3× 47 0.6× 14 269
Size Zheng China 8 185 1.0× 80 0.6× 82 0.7× 115 1.1× 36 0.4× 18 287
Oleksandr Zinenko United States 6 256 1.4× 120 1.0× 132 1.1× 58 0.5× 19 0.2× 11 373
Lingxiao Ma China 11 73 0.4× 68 0.5× 245 2.0× 219 2.0× 14 0.2× 16 349
Manu Shantharam United States 9 205 1.1× 182 1.5× 56 0.5× 26 0.2× 7 0.1× 18 289

Countries citing papers authored by Israt Nisa

Since Specialization
Citations

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

Fields of papers citing papers by Israt Nisa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Israt Nisa

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

All Works

16 of 16 papers shown
1.
Zheng, Da, Qi Zhu, Zichen Wang, et al.. (2024). GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications. 6356–6367. 3 indexed citations
2.
3.
Nisa, Israt, Minjie Wang, Da Zheng, et al.. (2023). Optimizing Irregular Dense Operators of Heterogeneous GNN Models on GPU. 32. 199–206. 2 indexed citations
4.
5.
Zheng, Da, et al.. (2022). Nimble GNN Embedding with Tensor-Train Decomposition. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2327–2335. 5 indexed citations
6.
Selvitopi, Oğuz, et al.. (2022). Parallel algorithms for masked sparse matrix-matrix products. 453–454. 5 indexed citations
7.
Nisa, Israt, et al.. (2021). Distributed-Memory k-mer Counting on GPUs. 527–536. 4 indexed citations
8.
Selvitopi, Oğuz, et al.. (2021). Distributed-memory parallel algorithms for sparse times tall-skinny-dense matrix multiplication. 431–442. 13 indexed citations
9.
Nisa, Israt, Jiajia Li, Aravind Sukumaran-Rajam, Richard Vuduc, & P. Sadayappan. (2019). Load-Balanced Sparse MTTKRP on GPUs. 123–133. 31 indexed citations
10.
Nisa, Israt, et al.. (2019). An efficient mixed-mode representation of sparse tensors. 1–25. 26 indexed citations
11.
Hong, Changwan, et al.. (2019). Adaptive sparse tiling for sparse matrix multiplication. 300–314. 99 indexed citations
12.
Nisa, Israt, Charles Siegel, Aravind Sukumaran-Rajam, Abhinav Vishnu, & P. Sadayappan. (2018). Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs. 1056–1065. 23 indexed citations
13.
Nisa, Israt, et al.. (2018). Sampled Dense Matrix Multiplication for High-Performance Machine Learning. 32–41. 15 indexed citations
14.
Hong, Changwan, Aravind Sukumaran-Rajam, Jinsung Kim, et al.. (2018). Efficient sparse-matrix multi-vector product on GPUs. 66–79. 43 indexed citations
15.
Sukumaran-Rajam, Aravind, et al.. (2017). On improving performance of sparse matrix-matrix multiplication on GPUs. 1–11. 16 indexed citations
16.
Nisa, Israt, et al.. (2017). Parallel CCD++ on GPU for Matrix Factorization. 73–83. 17 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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