| Predicting PM2. 5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data P Muthukumar, E Cocom, K Nagrecha, D Comer, I Burga, J Taub, ... Air Quality, Atmosphere & Health, 1-14, 2021 | 63 | 2021 |
| Cerebro: A layered data platform for scalable deep learning A Kumar, S Nakandala, Y Zhang, S Li, A Gemawat, K Nagrecha 11th Annual Conference on Innovative Data Systems Research (CIDR ‘21), 2021 | 36 | 2021 |
| PM2. 5 air pollution prediction through deep learning using multisource meteorological, wildfire, and heat data P Muthukumar, K Nagrecha, D Comer, CF Calvert, N Amini, J Holm, ... Atmosphere 13 (5), 822, 2022 | 27 | 2022 |
| Incremental and approximate computations for accelerating deep CNN inference S Nakandala, K Nagrecha, A Kumar, Y Papakonstantinou ACM Transactions on Database Systems (TODS) 45 (4), 1-42, 2020 | 27 | 2020 |
| As-Encountered Prediction of Tunnel Boring Machine Performance Parameters using Recurrent Neural Networks K Nagrecha, L Fisher, M Mooney, T Rodriguez-Nikl, M Mazari, ... Transportation Research Record 2674 (8), 2020 | 25 | 2020 |
| Gradient-based algorithms for machine teaching P Wang, K Nagrecha, N Vasconcelos Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 23 | 2021 |
| Model-Parallel Model Selection for Deep Learning Systems K Nagrecha Proceedings of the 2021 International Conference on Management of Data, 2929 …, 2021 | 20 | 2021 |
| Sensor-Based Air Pollution Prediction using Deep CNN-LSTM K Nagrecha, P Muthukumar, E Cocom, J Holm, D Comer, I Burga, ... 2020 International Conference on Computational Science and Computational …, 2020 | 19 | 2020 |
| Satellite image atmospheric air pollution prediction through meteorological graph convolutional network with deep convolutional LSTM P Muthukumar, E Cocom, K Nagrecha, J Holm, D Comer, A Lyons, I Burga, ... 2020 International Conference on Computational Science and Computational …, 2020 | 16 | 2020 |
| Systems for parallel and distributed large-model deep learning training K Nagrecha arXiv preprint arXiv:2301.02691, 2023 | 14 | 2023 |
| Intune: Reinforcement learning-based data pipeline optimization for deep recommendation models K Nagrecha, L Liu, P Delgado, P Padmanabhan Proceedings of the 17th ACM Conference on Recommender Systems, 430-442, 2023 | 7 | 2023 |
| Hydra: A System for Large Multi-Model Deep Learning K Nagrecha, A Kumar arXiv preprint arXiv:2110.08633, 2021 | 7 | 2021 |
| Predicting PM2. 5 air pollution using deep learning with multisource satellite and ground-based observations and meteorological and wildfire big data P Muthukumar, K Nagrecha, E Cocom, D Comer, I Burga, J Taub, ... AGU Fall Meeting Abstracts 2021, GC45B-0842, 2021 | 6 | 2021 |
| Saturn: An optimized data system for multi-large-model deep learning workloads K Nagrecha, A Kumar Proceedings of the VLDB Endowment 17 (4), 2023 | 5 | 2023 |
| Tech report of saturn: An optimized data system for multi-large model deep learning K Nagrecha, A Kumar May, 2023 | 5 | 2023 |
| Multi-pollutant ground-level air pollution prediction through deep meteogcn-convlstm P Muthukumar, S Pathak, K Nagrecha, H Hosseini, D Comer, N Amini, ... 2022 International Conference on Computational Science and Computational …, 2022 | 5 | 2022 |
| Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads (Information System Architectures) K Nagrecha, A Kumar | 2 | 2023 |
| Saturn: An Optimized Data System for Large Model Deep Learning Workloads K Nagrecha, A Kumar arXiv preprint arXiv:2309.01226, 2023 | 1 | 2023 |
| Optimizing deep learning recommender model data pipelines with reinforcement learning K Nagrecha, L Liu, PAD Aqueveque, P Padmanabhan US Patent App. 18/630,074, 2024 | | 2024 |
| Reinforcement Learning for Intra-& Inter-Node Recommender Data Pipeline Optimization K Nagrecha, L Liu, P Delgado ACM Transactions on Recommender Systems, 2024 | | 2024 |
Arun KumarUniversity of California, San DiegoVerified email at eng.ucsd.edu
