| Extraction and interpretation of deep autoencoder-based temporal features from wearables for forecasting personalized mood, health, and stress B Li, A Sano Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2020 | 84 | 2020 |
| Mapping solar array location, size, and capacity using deep learning and overhead imagery JM Malof, B Li, B Huang, K Bradbury, A Stretslov Preprint at https://arxiv. org/abs/1902.10895, 2019 | 30* | 2019 |
| Early versus late modality fusion of deep wearable sensor features for personalized prediction of tomorrow’s mood, health, and stress B Li, A Sano 2020 42nd Annual International Conference of the IEEE Engineering in …, 2020 | 24 | 2020 |
| Graph-based algorithm unfolding for energy-aware power allocation in wireless networks B Li, G Verma, S Segarra IEEE Transactions on Wireless Communications 22 (2), 1359-1373, 2022 | 23 | 2022 |
| What you get is not always what you see—pitfalls in solar array assessment using overhead imagery W Hu, K Bradbury, JM Malof, B Li, B Huang, A Streltsov, KS Fujita, B Hoen Applied Energy 327, 120143, 2022 | 18 | 2022 |
| Power allocation for wireless federated learning using graph neural networks B Li, A Swami, S Segarra ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 16 | 2022 |
| Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning Y Zhu, B Li, S Segarra 2021 29th European Signal Processing Conference (EUSIPCO), 2021 | 16 | 2021 |
| Deep demixing: Reconstructing the evolution of epidemics using graph neural networks G Čutura, B Li, A Swami, S Segarra 2021 29th European Signal Processing Conference (EUSIPCO), 2204-2208, 2021 | 12 | 2021 |
| Toward end-to-end prediction of future wellbeing using deep sensor representation learning B Li, H Yu, A Sano 2019 8th international conference on affective computing and intelligent …, 2019 | 8 | 2019 |
| Learnable digital twin for efficient wireless network evaluation B Li, T Efimov, A Kumar, J Cortes, G Verma, A Swami, S Segarra MILCOM 2023-2023 IEEE Military Communications Conference (MILCOM), 661-666, 2023 | 7 | 2023 |
| Hypergraph 1-spectral clustering with general submodular weights Y Zhu, B Li, S Segarra 2022 56th Asilomar Conference on Signals, Systems, and Computers, 935-939, 2022 | 5 | 2022 |
| Hypergraphs with edge-dependent vertex weights: spectral clustering based on the 1-laplacian Y Zhu, B Li, S Segarra ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 5 | 2022 |
| Energy-efficient power allocation in wireless networks using graph neural networks B Li, G Verma, C Rao, S Segarra 2021 55th Asilomar Conference on Signals, Systems, and Computers, 732-736, 2021 | 4 | 2021 |
| GLANCE: graph-based learnable digital twin for communication networks B Li, G Verma, T Efimov, A Kumar, S Segarra arXiv preprint arXiv:2408.09040, 2024 | 3 | 2024 |
| Learning to Transmit with Provable Guarantees in Wireless Federated Learning B Li, J Perazzone, A Swami, S Segarra IEEE Transactions on Wireless Communications, 2023 | 3 | 2023 |
| Deep Demixing: Reconstructing the Evolution of Network Epidemics B Li, G Čutura, A Swami, S Segarra arXiv preprint arXiv:2306.07938, 2023 | | 2023 |
| Influence of background lung characteristics on nodule detection with computed tomography B Li, TB Smith, KR Choudhury, B Harrawood, L Ebner, JE Roos, ... Journal of Medical Imaging 7 (2), 022409-022409, 2020 | | 2020 |
Santiago SegarraAssociate Professor, Electrical and Computer Engineering, Rice UniversityDirección de correo verificada de rice.edu
