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 | 34 | 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 | 29 | 2021 |
Gradient-based algorithms for machine teaching P Wang, K Nagrecha, N Vasconcelos Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 19 | 2021 |
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 | 19 | 2020 |
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 | 18 | 2020 |
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 | 17 | 2022 |
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 | 16 | 2020 |
Model-Parallel Model Selection for Deep Learning Systems K Nagrecha Proceedings of the 2021 International Conference on Management of Data, 2929 …, 2021 | 12 | 2021 |
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 | 9 | 2020 |
Systems for Parallel and Distributed Large-Model Deep Learning Training K Nagrecha arXiv preprint arXiv:2301.02691, 2023 | 6 | 2023 |
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 | 5 | 2021 |
Hydra: A System for Large Multi-Model Deep Learning K Nagrecha, A Kumar arXiv preprint arXiv:2110.08633, 2021 | 4 | 2021 |
Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads K Nagrecha, A Kumar Proceedings of the VLDB Endowment 17 (4), 712-725, 2023 | 3 | 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 | 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 |
Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads (Information System Architectures) K Nagrecha, A Kumar | 1 | 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 | 1 | 2022 |
Saturn: Efficient Multi-Large-Model Deep Learning K Nagrecha, A Kumar arXiv preprint arXiv:2311.02840, 2023 | | 2023 |
Prediction of geological composition using recurrent neural networks and shield tunnel boring machine data M Pourhomayoun, M Mazari, L Fisher, K Nagrecha, T Rodriguez-Nikl, ... Civil Engineering and Environmental Systems 40 (4), 252-266, 2023 | | 2023 |
Predicting Atmospheric Air Pollution: A Convolutional-Transformer Approach for Spatial and Temporal Analysis of PM2. 5 J Kalra, P Muthukumar, S Pathak, K Nagrecha, H Hosseini, D Comer, ... 2023 Congress in Computer Science, Computer Engineering, & Applied Computing …, 2023 | | 2023 |