Srinivas (Sai) Ravela

http://essg.mit.edu

Ph.D., Computer Science, University of Massachusetts, 2002

Dr. Ravela develops succinct data-driven computational methods for natural systems that emphasizes the dynamic coupling between phenomenology and physics, simulation and observation, humans and algorithms, and data with models. Current contributions include in the areas of modeling, inference, uncertainty quantification and cooperative autonomous observation of coherent fluids, fluid imaging, animal biometrics, natural hazard risk assessment and decision support. These contributions are enabled by methodological investigations in machine learning, nonlinear dynamics, stochastic systems science and computational physics.    

A. Karpatne, H. A. Babaie, S. Ravela, V. Kumar, I. Ebert-Uphoff (2017), "Machine Learning for the Geosciences - Opportunities, Challenges, and Implications for the ML process", In Mining Big Data in Climate and Environment, SIAM SDM



S. Ravela (2016), A Non-Parametric Framework for Inference Using Dynamically Deformed and Targeted Manifolds, MS41, pp 60, SIAM AM



Neumann, J. E., Emanuel, K. A., Ravela, S., Ludwig, L. C., & Verly, C. (2015). Risks of coastal storm surge and the effect of sea level rise in the Red River Delta, Vietnam. Sustainability, 7(6), 6553-6572, doi: 10.3390/su7066553



Ravela, S. (2015). Dynamic Data-driven Deformable Reduced Models for Coherent Fluids1. Procedia Computer Science, 51, 2464-2473, doi: 10.1016/j.procs.2015.05.355



Seybold, H., Ravela, S., & Tagade, P. (2015). Ensemble Learning in Non-Gaussian Data Assimilation. In Dynamic Data-Driven Environmental Systems Science (pp. 227-238). Springer International Publishing, doi: 10.1007/978-3-319-25138-7_21



Duyck, J. and Finn, C. and Hutcheon, A. and Vera, P. and Salas, J. and Ravela, S. (2015) Sloop: A pattern retrieval engine for individual animal identification Pattern Recognition, vol. 48, no. 4, pp. 1059-1073, doi: 10.1016/j.patcog.2014.07.017