Publications

2026

1. TTCD: Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data. O. Faruque, S. Ali, X. Zheng, J. Wang. arXiv preprint.
2. Identifying Energy Balance Drivers of Greenland Ice Sheet Surface Melt Using Causal Discovery. Z. Yin, A. C. Subramanian, R. Datta, A. R. Herrington, D. Du, S. Ali, O. Faruque, et al. Geophysical Research Letters.
3. Benchmarking Scientific Machine Learning Models for Air Quality Data. K. I. Masud, V. S. R. Unnam, S. Ali. arXiv preprint.
4. SmartSort Bin: AI-Based Solar-Powered System for Automated Waste Segregation Using IoT. M. Y. Zihad, K. I. Masud, A. Paul, M. S. Islam, M. O. Rahman, S. Ali. International Conference on Smart Multidomain Integrated Learning.

2025

1. TS-CausalNN: Learning Temporal Causal Relations from Non-Linear Non-Stationary Time Series Data. O. Faruque, S. Ali, X. Zheng, J. Wang. IEEE International Conference on Data Mining Workshops (ICDMW).
2. LLM-Enhanced Knowledge Discovery for Polar Data Science. A. K. Dugyala, H. Varanasi, S. Ali. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Polar Data.

2024

1. Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison. M. Bushuk, S. Ali, D. A. Bailey, et al. Bulletin of the American Meteorological Society (BAMS).
2. Causality for Earth Science: A Review on Time-series and Spatiotemporal Causality Methods. S. Ali, U. Hasan, X. Li, O. Faruque, A. Sampath, Y. Huang, M. O. Gani, J. Wang. arXiv preprint.
3. Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference. S. Ali, O. Faruque, J. Wang. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD).
4. Tutorial on Causal Inference with Spatiotemporal Data. S. Ali, J. Wang. Proceedings of the 1st ACM SIGSPATIAL International Workshop.
5. Incorporating Causality with Deep Learning in Predicting Short-Term and Seasonal Sea Ice. E. Hossain, S. Ali, Y. Huang, N. J. Schlegel, J. Wang, A. C. Subramanian, et al. 104th American Meteorological Society Annual Meeting.
6. Estimating Causal Effects of Greenland Blocking on Arctic Sea Ice Melt Using Deep Learning Technique. S. Ali, Y. Huang, M. O. Gani, N. J. Schlegel, A. Subramanian, J. Wang. 104th American Meteorological Society Annual Meeting.

2023

1. Quantifying Causes of Arctic Amplification via Deep Learning Based Time-Series Causal Inference. S. Ali, O. Faruque, Y. Huang, M. O. Gani, A. Subramanian, N. J. Schlegel, et al. International Conference on Machine Learning and Applications (ICMLA).
2. AI for Sea Ice Forecasting. S. Ali, Y. Huang, J. Wang. Book chapter in Artificial Intelligence in Earth Science.
3. Integrating Fourier Transform and Residual Learning for Arctic Sea Ice Forecasting. L. Lapp, S. Ali, J. Wang. International Conference on Machine Learning and Applications (ICMLA).

2022

1. MT-IceNet: A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting. S. Ali, J. Wang. IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT).
2. Benchmarking Probabilistic Machine Learning Models for Arctic Sea Ice Forecasting. S. Ali, S. A. M. Mostafa, X. Li, S. Khanjani, J. Wang, J. Foulds, V. Janeja. IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

2021

1. Sea Ice Forecasting Using Attention-Based Ensemble LSTM. S. Ali, Y. Huang, X. Huang, J. Wang. arXiv preprint.
2. Multi-Task Deep Learning Based Spatiotemporal Arctic Sea Ice Forecasting. E. Kim, P. Kruse, S. Lama, J. Bourne, M. Hu, S. Ali, Y. Huang, J. Wang. IEEE International Conference on Big Data.

2020

1. Deep Domain Adaptation Based Cloud Type Detection Using Active and Passive Satellite Data. X. Huang, S. Ali, C. Wang, Z. Ning, S. Purushotham, J. Wang, Z. Zhang. IEEE International Conference on Big Data.
2. Deep Multi-Sensor Domain Adaptation on Active and Passive Satellite Remote Sensing Data. X. Huang, S. Ali, S. Purushotham, J. Wang, C. Wang, Z. Zhang. 1st KDD Workshop on Deep Learning for Spatiotemporal Data.
3. Evaluation of Low-Cloud and Warm Rain Simulation in CMIP6 Models through Comparisons with Satellite Observations. A. Denagamage, Z. Zhang, M. Segal-Rosenhaimer, J. Wang, S. Ali, et al. AGU Fall Meeting Abstracts.