kEarth 🌏
AI for Earth Sciences and Earth Observations
Publications
Wealth over Woe: Global biases in hydro‐hazard research. Earth’s Future, 12(10), p.e2024EF004590. Stein, L., Mukkavilli, S.K., Pfitzmann, B.M., Staar, P.W., Ozturk, U., Berrospi, C., Brunschwiler, T. and Wagener, T. (2024).
Indus: Effective and efficient language models for scientific applications. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). (pp. 98–112). Association for Computational Linguistics. Bhattacharjee, B., Trivedi, A., Muraoka, M., Ramasubramanian, M., Udagawa, T., Gurung, I., Pantha, N., Zhang, R., Dandala, B., Ramachandran, R., Maskey, M., Bugbee, K., Little, M., Fancher, E., Gerasimov, I., Mehrabian, A., Sanders, L., Costes, S., Blanco-Cuaresma, S., Lockhart, K., Allen, T., Grezes, F., Ansdell, M., Accomazzi, A., El-Kurdi, Y., Wertheimer, D., Pfitzmann, B., Berrospi Ramis, C., Dolfi, M., Teixeira de Lima, R., Vagenas, P., Mukkavilli, S. K., Staar, P., Vahidinia, S., McGranaghan, R., & Lee, T. (2024).
Lifelines for a drowning science‐improving findability and synthesis of hydrologic publications. Hydrological Processes, 36(11), e14742. Stein, L., Mukkavilli, S. K., & Wagener, T. (2022).
AB2CD: AI for Building Climate Damage Classification and Detection. In Proceedings of the AAAI Symposium Series (Vol. 2, No. 1, pp. 115-123). Nitsche, M., Mukkavilli, S. K., Kühl, N., & Brunschwiler, T. (2023).
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network. NeurIPS Workshop: Tackling Climate Change with Machine Learning. Kurihana, T., Yeo, K., Szwarcman, D., Elmegreen, B., Mukkavilli, K., Schmude, J., & Klein, L. (2023). arXiv preprint arXiv:2312.13212.
Assessment of atmospheric aerosols from two reanalysis products over Australia. Atmospheric research, 215, 149-164. Mukkavilli, S. K., Prasad, A. A., Taylor, R. A., Huang, J., Mitchell, R. M., Troccoli, A., & Kay, M. J. (2019).
Chaired Workshops