| Sub-seasonal climate forecasting via machine learning: Challenges, analysis, and advances S He, X Li, T DelSole, P Ravikumar, A Banerjee Proceedings of the AAAI Conference on Artificial Intelligence 35 (1), 169-177, 2021 | 54* | 2021 |
| Learning and dynamical models for sub-seasonal climate forecasting: Comparison and collaboration S He, X Li, L Trenary, BA Cash, T DelSole, A Banerjee Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 4495-4503, 2022 | 12 | 2022 |
| Interpretable predictive modeling for climate variables with weighted lasso S He, X Li, V Sivakumar, A Banerjee Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 1385-1392, 2019 | 7 | 2019 |
| Flight mode recognition method of the unmanned aerial vehicle based on telemetric data S He, D Liu, Y Peng Chinese journal of scientific instrument 37 (9), 2004-2013, 2016 | 6* | 2016 |
| Machine learning and dynamical models for sub-seasonal climate forecasting S He, X Li, L Trenary, BA Cash, T DelSole, A Banerjee NeurIPS workshop on Machine Learning and the Physical Sciences, 2021 | 2 | 2021 |
| High-dimensional dependency structure learning for physical processes J Golmohammadi, I Ebert-Uphoff, S He, Y Deng, A Banerjee 2017 IEEE International Conference on Data Mining (ICDM), 883-888, 2017 | 2 | 2017 |
| Flight data anomaly detection and diagnosis with variable association change S He, H Huang, S Yoo, W Yan, F Xue, T Wang, C Xu Proceedings of the 36th Annual ACM Symposium on Applied Computing, 346-354, 2021 | 1 | 2021 |
| Fault diagnosis for discrete monitoring data based on fusion algorithm H Sijie, P Yu, L Datong 2015 12th IEEE International Conference on Electronic Measurement …, 2015 | 1 | 2015 |
| Advancing Climate Science with Machine Learning S He University of Minnesota, 2022 | | 2022 |
