Simulation of High-Resolution Precipitable Water Data by a Stochastic Model with a Random Trigger. - Kimberly Leung; Max Velado; Aneesh Subramanian; Guang J. Zhang; Richard C. J. Somerville; Samuel S. P. Shen (Adv. Data Sci. Adapt. Anal., 2016)

Statistical Characteristics of Long-Term High-Resolution Precipitable Water Vapor Data at Darwin. - Kimberly Leung; Aneesh Subramanian; Samuel S. P. Shen (Adv. Data Sci. Adapt. Anal., 2018)

Quantifying Causes of Arctic Amplification via Deep Learning Based Time-Series Causal Inference. - Sahara Ali; Omar Faruque; Yiyi Huang; Md. Osman Gani; Aneesh Subramanian; Nicole-Jeanne Schlegel; Jianwu Wang 001 (ICMLA, 2023)

STint: Self-supervised Temporal Interpolation for Geospatial Data. - Nidhin Harilal; Bri-Mathias S. Hodge; Aneesh Subramanian; Claire Monteleoni (CoRR, 2023)

Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach. - Subhankar Ghosh; Shuai An; Arun Sharma 006; Jayant Gupta; Shashi Shekhar 001; Aneesh Subramanian (CoRR, 2023)

Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling: A Summary of Results. - Subhankar Ghosh; Arun Sharma 006; Jayant Gupta; Aneesh Subramanian; Shashi Shekhar 001 (SIGSPATIAL/GIS, 2024)

Time Series Classification of Supraglacial Lakes Evolution over Greenland Ice Sheet. - Emam Hossain; Md. Osman Gani; Devon Dunmire; Aneesh Subramanian; Hammad Younas (CoRR, 2024)

Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling. - Subhankar Ghosh; Arun Sharma 006; Jayant Gupta; Aneesh Subramanian; Shashi Shekhar 001 (CoRR, 2024)

Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction. - Emam Hossain; Muhammad Hasan Ferdous; Jianwu Wang 001; Aneesh Subramanian; Md. Osman Gani (PerCom Workshops, 2025)

Engaging K-12 Learners in Data Annotation for AI Climate Models. - Michael MacFerrin; Edward Boyda; Kimberly Young; Josephine M. Namayanja; Aneesh Subramanian; Mohamed F. Mokbel; Lujie Karen Chen; Vandana P. Janeja (SIGCSE (2), 2025)

Building Machine Learning Challenges for Anomaly Detection in Science. - Elizabeth G. Campolongo; Yuan-Tang Chou; Ekaterina Govorkova; Wahid Bhimji; Wei-Lun Chao; Chris Harris; Shih-Chieh Hsu; Hilmar Lapp; Mark S. Neubauer; Josephine M. Namayanja; Aneesh Subramanian; Philip C. Harris; et al. (CoRR, 2025)