| Texture optimization for example-based synthesis V Kwatra, I Essa, A Bobick, N Kwatra ACM Siggraph 2005 Papers, 795-802, 2005 | 939 | 2005 |
| Gandiva: Introspective cluster scheduling for deep learning W Xiao, R Bhardwaj, R Ramjee, M Sivathanu, N Kwatra, Z Han, P Patel, ... 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2018 | 560 | 2018 |
| Two-way coupled SPH and particle level set fluid simulation F Losasso, J Talton, N Kwatra, R Fedkiw IEEE transactions on visualization and computer graphics 14 (4), 797-804, 2008 | 330 | 2008 |
| A method for avoiding the acoustic time step restriction in compressible flow N Kwatra, J Su, JT Grétarsson, R Fedkiw Journal of Computational Physics 228 (11), 4146-4161, 2009 | 183 | 2009 |
| Balancing efficiency and fairness in heterogeneous GPU clusters for deep learning S Chaudhary, R Ramjee, M Sivathanu, N Kwatra, S Viswanatha Proceedings of the Fifteenth European Conference on Computer Systems, 1-16, 2020 | 147 | 2020 |
| Varuna: scalable, low-cost training of massive deep learning models S Athlur, N Saran, M Sivathanu, R Ramjee, N Kwatra Proceedings of the Seventeenth European Conference on Computer Systems, 472-487, 2022 | 101 | 2022 |
| Respirenet: A deep neural network for accurately detecting abnormal lung sounds in limited data setting S Gairola, F Tom, N Kwatra, M Jain 2021 43rd Annual International Conference of the IEEE Engineering in …, 2021 | 99 | 2021 |
| Texturing fluids V Kwatra, D Adalsteinsson, T Kim, N Kwatra, M Carlson, M Lin IEEE transactions on visualization and computer graphics 13 (5), 939-952, 2007 | 91 | 2007 |
| A framework for activity recognition and detection of unusual activities D Mahajan, N Kwatra, S Jain, P Kalra, S Banerjee Indian Conference on Computer Vision, Graphics and Image Processing 20, 2004 | 72 | 2004 |
| Numerically stable fluid–structure interactions between compressible flow and solid structures JT Grétarsson, N Kwatra, R Fedkiw Journal of Computational Physics 230 (8), 3062-3084, 2011 | 63 | 2011 |
| Sarathi: Efficient llm inference by piggybacking decodes with chunked prefills A Agrawal, A Panwar, J Mohan, N Kwatra, BS Gulavani, R Ramjee arXiv preprint arXiv:2308.16369, 2023 | 51 | 2023 |
| Taming throughput-latency tradeoff in llm inference with sarathi-serve A Agrawal, N Kedia, A Panwar, J Mohan, N Kwatra, BS Gulavani, ... arXiv preprint arXiv:2403.02310, 2024 | 40 | 2024 |
| Fluid simulation with articulated bodies N Kwatra, C Wojtan, M Carlson, IA Essa, PJ Mucha, G Turk IEEE Transactions on Visualization and Computer Graphics 16 (1), 70-80, 2009 | 34 | 2009 |
| Unsupervised clustering using pseudo-semi-supervised learning D Gupta, R Ramjee, N Kwatra, M Sivathanu International Conference on Learning Representations, 2020 | 30 | 2020 |
| Singularity: Planet-scale, preemptive and elastic scheduling of AI workloads D Shukla, M Sivathanu, S Viswanatha, B Gulavani, R Nehme, A Agrawal, ... arXiv preprint arXiv:2202.07848, 2022 | 28 | 2022 |
| Practical Animation of Compressible Flow for ShockWaves and Related Phenomena. N Kwatra, J Gretarsson, R Fedkiw Symposium on Computer Animation, 207-215, 2010 | 26 | 2010 |
| Wide-minima density hypothesis and the explore-exploit learning rate schedule N Iyer, V Thejas, N Kwatra, R Ramjee, M Sivathanu Journal of Machine Learning Research 24 (65), 1-37, 2023 | 24 | 2023 |
| SmartKC: smartphone-based corneal topographer for keratoconus detection S Gairola, M Bohra, N Shaheer, N Jayaprakash, P Joshi, ... Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2021 | 16 | 2021 |
| Asynchronous evolution for fully‐implicit and semi‐implicit time integration C Schroeder, N Kwatra, W Zheng, R Fedkiw Computer Graphics Forum 30 (7), 1983-1992, 2011 | 12 | 2011 |
| Vidur: A Large-Scale Simulation Framework For LLM Inference A Agrawal, N Kedia, J Mohan, A Panwar, N Kwatra, B Gulavani, ... Proceedings of Machine Learning and Systems 6, 351-366, 2024 | 11 | 2024 |
Ronald FedkiwProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
