Publications

V. Tombs, J. Wohlgemuth, A. K. Patra, D. Lu, and S. Guggilam (2024). AI/ML assurance: Applications in geospatial sciences . AGU23, AGU .

P. A. Dias, T. Kobayashi-Carvalhaes, S. Walters, T. Frazier, C. Woody, S. Guggilam, D. Adams, A. Potnis, and D. Lunga (2024). GeoAI for humanitarian assistance . Handbook of Geospatial Artificial Intelligence, CRC Press .

K. Harrod, S. Guggilam, H. Tubbs, and A. Anyamba (2024). Climate variable tipping points for rift valley fever outbreaks . AGU24 .

M. Mohan, N. Gugulothu, S. Guggilam, T. R. Rajeshwar, M. K. Kidder, and J. C. Smith (2024). Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution . Green Chemical Engineering .

M. Mohan, K. D. Jetti, S. Guggilam, M. D. Smith, M. K. Kidder, and J. C. Smith (2024). High-throughput screening and accurate prediction of ionic liquid viscosities using interpretable machine learning . ACS Sustainable Chemistry & Engineering, vol. 12, no. 18, pp. 7040–7054 .

P. Dias, A. Potnis, S. Guggilam, L. Yang, A. Tsaris, H. Medeiros, and D. Lunga (2023). An agenda for multimodal foundation models for earth observation . IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, IEEE .

S. Guggilam, V. Chandola, and A. K. Patra (2023). Large deviations anomaly detection (LAD) for collection of multivariate time series data: Applications to COVID-19 data . Journal of Computational Science, vol. 72, p. 102 101 .

P. Lougovski, O. Parekh, J. Broz, M. Byrd, Y. Chembo, W. A. de Jong, E. Figueroa, T. S. Humble, J. Larson, G. Quiroz, et al. (2023). Position papers for the ASCR workshop on basic research needs in quantum computing and networking . USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR), Tech. Rep. .

S. Gaikwad, S. Iyer, D. Lunga, T. Yabe, X. Liang, B. Ananthabhotla, N. Behari, S. Guggilam, and G. Chi (2022). Data-driven humanitarian mapping and policymaking: Toward planetary-scale resilience, equity, and sustainability . Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining .

S. Guggilam, V. Chandola, and A. Patra (2022). Tracking clusters and anomalies in evolving data streams . Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 15, no. 2, pp. 156–178 .

S. Guggilam, V. Chandola, and A. K. Patra (2022). Classifying anomalous members in a collection of multivariate time series data using large deviations principle: An application to COVID-19 data . International Conference on Computational Science, Springer .

S. Guggilam, V. Chandola, and A. Patra (2021). Anomaly detection for high-dimensional data using large deviations principle . arXiv preprint arXiv:2109.13698 .

S. Guggilam, S. M. A. Zaidi, V. Chandola, and A. K. Patra (2019). Integrated clustering and anomaly detection (INCAD) for streaming data . Computational Science–ICCS 2019: 19th International Conference, Springer International Publishing .

S. Guggilam, S. Zaidi, V. Chandola, and A. Patra (2019). Bayesian anomaly detection using extreme value theory . arXiv preprint arXiv:1905.12150 .