| 2026 |
| Wang, Z., Roy, S., Maskey, M. and Ramachandran, R., 2026. The exact LCU sampling overhead of collective diagonal unitaries: resonances and a continued-fraction dichotomy. arXiv preprint arXiv:2610.03595. https://doi.org/10.48550/arXiv.2610.03595 |
| Heras, D. B., Sedona, R., Ordóñez, Á., Cavallaro, G., López, S., Maskey, M., Meoni, G., Miroszewski, A., Schuman, C. D., & Benediktsson, J. A. (2026). A comprehensive survey on high-performance and efficient computing for Earth observation. Proceedings of the IEEE. Advance online publication. https://doi.org/10.1109/jproc.2026.3721381 |
| Wang, Z., Roy, S., Akbari, A., Maskey, M., & Ramachandran, R. (2026). Structured quantum kernels for chaotic forecasting (arXiv Preprint No. 2609.13360). arXiv. https://doi.org/10.48550/arXiv.2609.13360 |
| Fraccaro, P., Nyirjesy, G., Szwarcman, D., Patil, H., Gaur, V., Lal, R., Slank, R., Dawson, G., Debary, H., Barker, M., Annex, A., Viswanathan, V., Morse, Z., Schaefer, E., Dionelis, N., Kumar, A., Watson, C., Maskey, M., Dawson-Rigas, R., Bernabe-Moreno, J., Ramachandran, R., & Roy, S. (2026) Multimodal-multiresolution foundation model for lunar remote sensing (arXiv:2609.13283v1). arXiv. https://arxiv.org/html/2609.13283v1 |
| Patil, H., Nyirjesy, G., Slank, R., Gaur, V., Szwarcman, D., Fraccaro, P., Dionelis, N., Barker, M., Annex, A., Viswanathan, V., Morse, Z., Schaefer, E., Debary, H., Kumar, A., Lal, R., Dawson, G., Watson, C., Dawson-Rigas, R., Maskey, M., Bernabe-Moreno, J., Ramachandran, R., & Roy, S. (2026). SomBench: benchmark dataset for advancing machine learning in lunar science (arXiv:2609.13277v1). arXiv. https://arxiv.org/html/2609.13277v1 |
| Pantha, N., Kumbam, P. R., Awale, S., Krishnappa, P., Ramasubramanian, M., Jha, N., Foshee, E., Kumar, A., Slank, R., Danehkar, A., & Ramachandran, R. (2026).Scientific code search at scale: A multi-domain dataset and benchmark (arXiv:2607.05443). arXiv. https://doi.org/10.48550/arXiv.2607.05443 |
| Kolluru, V., Shinde, R., Marouane, A., Helbling, C., Shah, D., Drew, O., Gurung, I., Maskey, M., & Ramachandran, R. (2026).Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events (arXiv:2606.07780). arXiv. https://doi.org/10.48550/arXiv.2606.07780 |
| Roy, S., Nair, U., Wu, Y., Priftis, G., Wang, L., Georgiou, A., Jones, A., Lütjens, B., Schmude, J., Watson, C., Slank, R. A., Kumar, A., Mukherjee, A., Sen, P., Lolachi, R., Chen, H., Maskey, M., Bernabé-Moreno, J., & Ramachandran, R. (2026). Towards a foundation model for the Martian atmosphere (arXiv:2605.28851). arXiv. https://doi.org/10.48550/arXiv.2605.28851 |
| Ramachandran, R., Bugbee, K., & Bernabe‐Moreno, J. (2026).Accelerated knowledge discovery: A vision for NASA science. Earth and Space Science, 13(3), Article e2025EA004662. https://doi.org/10.1029/2025EA004662 |
| Shinde, R., Ankur, K., Phillips, C. E., Gupta, A., Pfreundschuh, S., Roy, S., Kirkland, S., Gaur, V., Lin, A., Sheshadri, A., Nair, U., Maskey, M., & Ramachandran, R. (2026). WxC-Bench: A novel dataset for weather and climate downstream tasks. Scientific Data, 13(1). https://doi.org/10.1038/s41597-026-06839-7 |
| Roy, S., Hegde, D. V., Schmude, J., Lin, A., Gaur, V., Lal, R., Mandal, K., Singh, T., Muñoz-Jaramillo, A., Yang, K., Pandey, C., Hong, J., Aydin, B., McGranaghan, R., Kasapis, S., Upendran, V., Bahauddin, S., da Silva, D., Freitag, M., Gurung, I., Pogorelov, N., Watson, C., Maskey, M., Bernabe-Moreno, J., & Ramachandran, R. (2026). SuryaBench: Benchmark dataset for advancing machine learning in heliophysics and space weather prediction. Scientific Data, 13(1). https://doi.org/10.1038/s41597-026-06552-5 |
| Schmude, J., Roy, S., Wang, L., van Kessel, T., Klein, L., Freitag, M., Bentivegna, E., Manson-Sawko, R., Lütjens, B., Maskey, M., Watson, C., Ramachandran, R., & Bernabe-Moreno, J. (2026).PDE foundation models are skillful AI weather emulators for the Martian atmosphere(arXiv:2602.15004). arXiv. https://doi.org/10.48550/arXiv.2602.15004 |
| Ramachandran, R., Jha, N., & Ramasubramanian, M. (2026). Collaborative Agent Reasoning Engineering (CARE): A structured three-party design methodology for systematically engineering AI agents with SMEs, developers, and helper agents(Technical Memorandum). National Aeronautics and Space Administration. https://doi.org/10.64631/TAXQ7736 |
| Pfreundschuh, S., Kummerow, C.D., Schmude, J., Roy, S., Ramachandran, R., Lee, T., Anantharaj, V. and Breen, K.H. (2026). Prithvi-Precip: Integrating Satellite Observations into an Atmospheric AI Foundation Model for Precipitation Forecasting. arXiv. https://doi.org/10.48550/arXiv.2608.03959 |
| Khallaghi, S., Singh, R., Abedi, R., Eastman, J.R., Estes, L.D., Roy, S., Ramachandran, R. and Alemohammad, H. (2026). Assessing the robustness of Prithvi Geospatial Foundation Model for coastal habitat mapping under data availability and domain shift scenarios. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2026.3698337 |
| Blackford, A., Nair, U., Cotton, R., Phillips, C., Kaulfus, A., and Freitag, B. (2026). Land–atmosphere interaction responses of burn scar heat islands: a case study of the 2018 Camp Fire. Bulletin of the American Meteorological Society, 107(3), E446-E465. https://doi.org/10.1175/BAMS-D-24-0336.1 |
| Wang, Z., Roy, S., Maskey, M. and Ramachandran, R., 2026. The exact LCU sampling overhead of collective diagonal unitaries: resonances and a continued-fraction dichotomy. arXiv preprint arXiv:2610.03595. |
| 2025 |
| Longépé, N., Alemohammad, H., Anghelea, A., Brunschwiler, T., Camps-Valls, G., Cavallaro, G., Chanussot, J., Delgado, J. M., Demir, B., Dionelis, N., Fraccaro, P., Jungbluth, A., Kennedy, R. E., Marsocci, V., Ramasubramanian, M., Ramos-Pollan, R., Roy, S., Sümbül, G., Tuia, D., Zhu, X. X., & Ramachandran, R. (2025).Earth action in transition: Highlights from the 2025 ESA–NASA International Workshop on AI Foundation Models for EO. IEEE Geoscience and Remote Sensing Magazine, 13(4), 457–462. https://doi.org/10.1109/MGRS.2025.3592035 |
| Bugbee, K., & Ramachandran, R. (2025). From tape reels to global access: A history and future vision of NASA’s scientific data management. Earth and Space Science, 12(9), Article e2025EA004413. https://doi.org/10.1029/2025EA004413 |
| Roy, S., Schmude, J., Lal, R., Gaur, V., Freitag, M., Kuehnert, J., van Kessel, T., Hegde, D. V., Muñoz-Jaramillo, A., Jakubik, J., Vos, E., Mandal, K., Asanjan, A. A., Almeida, J. L. D. S., Lin, A., Singh, T., Yang, K., Pandey, C., Hong, J., Aydin, B., Kurth, T., McGranaghan, R., Kasapis, S., Upendran, V., Bahauddin, S., Maskey, M., & Ramachandran, R. (2025).Surya: Foundation model for heliophysics (arXiv:2508.14112). arXiv. https://doi.org/10.48550/arXiv.2508.14112 |
| Ramachandran, R., Roy, S., Maskey, M., Szwarcman, D., & Fraccaro, P. (2025).A primer for assessing foundation models for Earth observation. Zenodo. https://doi.org/10.5281/zenodo.15264752 |
| Ramachandran, R., & Bugbee, K. (2025). Balancing practical uses and ethical concerns: The role of large language models in scientific research. Perspectives of Earth and Space Scientists, 6(1), Article e2024CN000258. https://doi.org/10.1029/2024CN000258 |
| Szwarcman, D., Roy, S., Fraccaro, P., Gíslason, O.E., Blumenstiel, B., Ghosal, R., De Oliveira, P.H., de Sousa Almeida, J.L., Sedona, R., Kang, Y. & Chakraborty, S. (2025). Prithvi-eo-2.0: A versatile multi-temporal foundation model for earth observation applications. IEEE Transactions on Geoscience and Remote Sensing. doi: 10.1109/TGRS.2025.3642610 |
| Li, W., Wang, S., Lee, H., Lu, C., Roy, S., Ramachandran, R. & Hsu, C.Y. (2025). Landslide hazard mapping with geospatial foundation models: Geographical generalizability, data scarcity, and band adaptability. (arXiv:2511.04474). arXiv. https://doi.org/10.48550/arXiv.2511.04474 |
| Gupta, A., Sheshadri, A., Roy, S., & Anantharaj, V. (2025). Offline performance of a nonlocal deep learning parameterization for climate model representation of atmospheric gravity waves. Journal of Advances in Modeling Earth Systems, 17, e2025MS004977. https://doi.org/10.1029/2025MS004977 |
| Gupta, A., Sheshadri, A., Roy, S., Schmude, J., Gaur, V., Leong, W.J., Maskey, M. & Ramachandran, R. (2025). Finetuning AI foundation models to develop subgrid‐scale parameterizations: A case study on atmospheric gravity waves. Journal of Advances in Modeling Earth Systems, 17(11), p.e2025MS005075. https://doi.org/10.1029/2025MS005075 |
| Ankur, K., Roy, S., Phillips, C.E., Nair, U., Maskey, M. & Ramachandran, R. (2025). Advancing hurricane forecasting with AI models for track and intensity prediction. Journal of Advances in Modeling Earth Systems, 17(9), p.e2024MS004822. https://doi.org/10.1029/2024MS004822 |
| Ju, J., Zhou, Q., Freitag, B., Roy, D., Zhang, H., Sridhar, M., Mandel, J., Arab, S., Schmidt, G., Crawford, C., Gascon, F., Strobl, P., Masek, J., and Neigh, C. (2025). The Harmonized Landsat and Sentinel-2 version 2.0 surface and reflectance dataset. Remote Sensing of Environment, Volume 324, 114723, ISSN 0034-4257. https://doi.org/10.1016/j.rse.2025.114723 |
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| 2024 |
| Bugbee, K., Ramachandran, R., Kaulfus, A., Le Roux, J., Peng, G., Smith, D., Gurung, I., Acharya, A., & Christman, J. (2024).Enabling dynamic data governance in science: Design, implementation, and future directions of the modern data governance framework. IGARSS 2024 – 2024 IEEE International Geoscience and Remote Sensing Symposium, 3720–3723. https://doi.org/10.1109/IGARSS53475.2024.10640434 |
| Oala, L., Maskey, M., Bat-Leah, L., Parrish, A., Gürel, N. M., Kuo, T.-S., Liu, Y., Dror, R., Brajovic, D., Yao, X., Bartolo, M., Rojas, W. A. G., Hileman, R., Aliment, R., Mahoney, M. W., Risdal, M., Lease, M., Samek, W., Dutta, D., Northcutt, C. G., Coleman, C., Hancock, B., Koch, B., Tadesse, G. A., & Karlaš, B. (2023). DMLR: Data-centric machine learning research – Past, present and future(arXiv:2311.13028). arXiv. https://doi.org/10.48550/arXiv.2311.13028 |
| Maskey, M., Parajuli, P., Shinde, R., Gurung, I., & Ramachandran, R. (2023).Rethinking AI for science: An evolution from data-driven to data-centric framework. Earth and Space Science, 10(12), e2023CN000222. https://doi.org/10.1029/2023CN000222 |
| Maskey, M., Alemohammad, H., Murphy, K. J., & Ramachandran, R. (2022). Advancing AI for Earth science through open data and collaboration. IEEE Geoscience and Remote Sensing Magazine, 10(1), 260–265. https://doi.org/10.1109/MGRS.2021.3138072 |
| Blackford, A., Cowan, T., Nair, U., Phillips, C., Kaulfus, A., & Freitag, B. (2024). Synergy of urban heat, pollution, and social vulnerability in one of America’s most rapidly growing cities: Houston, we have a problem. GeoHealth, 8(9), e2024GH001079. https://doi.org/10.1029/2024GH001079 |
| Freitag, B. M., Maskey, M., Barciauskas, A., Solvsteen, J., Colliander, J., & Munroe, J. (2024).The Visualization, Exploration, and Data Analysis (VEDA) Platform: A modular, open platform lowering the barrier to entry to cloud computing. IGARSS 2024 – 2024 IEEE International Geoscience and Remote Sensing Symposium, 3736–3739. https://doi.org/10.1109/IGARSS53475.2024.10641425 |
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| 2023 |
| Jakubik, J., Roy, S., Phillips, C. E., Fraccaro, P., Godwin, D., Zadrozny, B., Szwarcman, D., Gomes, C., Nyirjesy, G., Edwards, B., Kimura, D., Simumba, N., Chu, L., Mukkavilli, S. K., Lambhate, D., Das, K., Bangalore, R., Oliveira, D., Muszynski, M., Ankur, K., Ramasubramanian, M., Gurung, I., Khallaghi, S., Li, H., & Maskey, M. (2023). Foundation models for generalist geospatial artificial intelligence (arXiv:2310.18660). arXiv. https://doi.org/10.48550/arXiv.2310.18660 |
| Maskey, M., Ramachandran, R., Freitag, B., Kaulfus, A., Barciauskas, A., Veerman, O., Thomas, L., Gurung, I., & Ramasubramanian, M. (2023). Dashboard for Earth Observation. In S. S. Durbha, J. Sanyal, L. Yang, S. S. Chaudhari, U. Bhangale, U. Bharambe, & K. Kurte (Eds.), Advances in Scalable and Intelligent Geospatial Analytics: Challenges and Applications (pp. 219–230). CRC Press. https://doi.org/10.1201/9781003270928-15 |
| Bugbee, K., Smith, D., Wingo, S., & Foshee, E. (2023). The art of scientific curation. Eos, Transactions American Geophysical Union, 104, 15–19. |
| Gurung, I., Ramasubramanian, M., Freitag, B., Kaulfus, A., Maskey, M., Ramachandran, R., & Alemohammad, H. (2023). Tropical cyclone wind speed estimation: A large scale training data set and community benchmarking. Earth and Space Science, 10(3), e2022EA002693. https://doi.org/10.1029/2022EA002693 |
| Maskey, M. (2023). Rethinking AI for science: An evolution from data-driven to data-centric framework. Perspectives of Earth and Space Scientists, 4, e2023CN000222. https://doi.org/10.1029/2023CN000222 |
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| 2022 |
| Ramachandran, R., Maskey, M., Lynnes, C., John, A., & Mukherjee, T. (2022). Investigating different data-traceability approaches to prevent data swamps [Perspectives]. IEEE Geoscience and Remote Sensing Magazine, 10(3), 332–337. https://doi.org/10.1109/MGRS.2022.3203865 |
| Ramachandran, R., Ramasubramanian, M., Koirala, P., Gurung, I., & Maskey, M. (2022). Language model for Earth science: Exploring potential downstream applications as well as current challenges. IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), 4687–4690. https://doi.org/10.1109/IGARSS46834.2022.9883682 |
| Gahlot, S., Ramasubramanian, M., Gurung, I., Hansch, R., Molthan, A., & Maskey, M. (2022). Curating flood extent data and leveraging citizen science for benchmarking machine learning solutions. ESS Open Archive. 13 April 2022. https://doi.org/10.1002/essoar.10511103.1 |
