On improving performance of sparse matrix-matrix multiplication on GPUs. - Rakshith Kunchum; Ankur Chaudhry; Aravind Sukumaran-Rajam; Qingpeng Niu; Israt Nisa; P. Sadayappan (ICS, 2017)

Parallel CCD++ on GPU for Matrix Factorization. - Israt Nisa; Aravind Sukumaran-Rajam; Rakshith Kunchum; P. Sadayappan (GPGPU@PPoPP, 2017)

Sampled Dense Matrix Multiplication for High-Performance Machine Learning. - Israt Nisa; Aravind Sukumaran-Rajam; Süreyya Emre Kurt; Changwan Hong; P. Sadayappan (HiPC, 2018)

Efficient sparse-matrix multi-vector product on GPUs. - Changwan Hong; Aravind Sukumaran-Rajam; Bortik Bandyopadhyay; Jinsung Kim; Süreyya Emre Kurt; Israt Nisa; Shivani Sabhlok; Ümit V. Çatalyürek; Srinivasan Parthasarathy 001; P. Sadayappan (HPDC, 2018)

Parallel Latent Dirichlet Allocation on GPUs. - Gordon Euhyun Moon; Israt Nisa; Aravind Sukumaran-Rajam; Bortik Bandyopadhyay; Srinivasan Parthasarathy 001; P. Sadayappan (ICCS (2), 2018)

Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs. - Israt Nisa; Charles Siegel; Aravind Sukumaran-Rajam; Abhinav Vishnu; P. Sadayappan (IPDPS Workshops, 2018)

Load-Balanced Sparse MTTKRP on GPUs. - Israt Nisa; Jiajia Li 001; Aravind Sukumaran-Rajam; Richard W. Vuduc; P. Sadayappan (IPDPS, 2019)

Adaptive sparse tiling for sparse matrix multiplication. - Changwan Hong; Aravind Sukumaran-Rajam; Israt Nisa; Kunal Singh; P. Sadayappan (PPoPP, 2019)

An efficient mixed-mode representation of sparse tensors. - Israt Nisa; Jiajia Li 001; Aravind Sukumaran-Rajam; Prashant Singh Rawat; Sriram Krishnamoorthy; P. Sadayappan (SC, 2019)

Distributed-memory parallel algorithms for sparse times tall-skinny-dense matrix multiplication. - Oguz Selvitopi; Benjamin Brock; Israt Nisa; Alok Tripathy; Katherine A. Yelick; Aydin Buluç (ICS, 2021)

Distributed-Memory k-mer Counting on GPUs. - Israt Nisa; Prashant Pandey 001; Marquita Ellis; Leonid Oliker; Aydin Buluç; Katherine A. Yelick (IPDPS, 2021)

Parallel Algorithms for Masked Sparse Matrix-Matrix Products. - Srdan Milakovic; Oguz Selvitopi; Israt Nisa; Zoran Budimlic; Aydin Buluç (ICPP, 2022)

Nimble GNN Embedding with Tensor-Train Decomposition. - Chunxing Yin; Da Zheng; Israt Nisa; Christos Faloutsos; George Karypis; Richard W. Vuduc (KDD, 2022)

TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs. - Hongkuan Zhou; Da Zheng; Israt Nisa; Vasileios Ioannidis; Xiang Song 003; George Karypis (CoRR, 2022)

TGL: A General Framework for Temporal GNN Training onBillion-Scale Graphs. - Hongkuan Zhou; Da Zheng; Israt Nisa; Vassilis N. Ioannidis; Xiang Song 003; George Karypis (Proc. VLDB Endow., 2022)

Optimizing Irregular Dense Operators of Heterogeneous GNN Models on GPU. - Israt Nisa; Minjie Wang; Da Zheng; Qiang Fu; Ümit V. Çatalyürek; George Karypis (IPDPS Workshops, 2023)

GraphStorm an Easy-to-use and Scalable Graph Neural Network Framework: From Beginners to Heroes. - Jian Zhang; Da Zheng; Xiang Song 003; Theodore Vasiloudis; Israt Nisa; Jim Lu (KDD, 2023)

PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks. - Kun Wu 002; Mert Hidayetoglu; Xiang Song 003; Sitao Huang; Da Zheng; Israt Nisa; Wen-Mei W. Hwu (CoRR, 2023)

Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures. - Kun Wu 002; Mert Hidayetoglu; Xiang Song 003; Sitao Huang; Da Zheng; Israt Nisa; Wen-Mei Hwu (ASPLOS (3), 2024)

GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications. - Da Zheng; Xiang Song 003; Qi Zhu 008; Jian Zhang; Theodore Vasiloudis; Runjie Ma; Houyu Zhang; Zichen Wang 002; Soji Adeshina; Israt Nisa; Alejandro Mottini; Qingjun Cui; et al. (KDD, 2024)

GraphStorm: all-in-one graph machine learning framework for industry applications. - Da Zheng; Xiang Song 003; Qi Zhu 008; Jiani Zhang 003; Theodore Vasiloudis; Runjie Ma; Houyu Zhang; Zichen Wang 002; Soji Adeshina; Israt Nisa; Alejandro Mottini; Qingjun Cui; et al. (CoRR, 2024)