Sai Ravela

TL;DR: The probability that hurricane winds will affect any given point in space by combining an estimate of the probability that a hurricane will pass within some given radius of the point in question with an estimates of the spatial probability density of storm winds.

TL;DR: Some of the cross-cutting research themes in machine learning that are applicable across several geoscience problems, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines are discussed.

TL;DR: This paper describes a system architecture and deployment to meet the design requirements and to allow model-driven control, thereby optimizing the prediction capability of the system.

TL;DR: A pattern recognition algorithm and photo-identification method that enables efficient identification of individual marbled salamanders in a database exceeding 1000 images is developed and scaled to larger databases, allowing biologists to address critical conservation-based questions regarding demography, reproduction and dispersal of rare and endangered species.

TL;DR: In this paper, the authors combined three models (a tropical cyclone simulation model, a storm surge model, and a model for economic impact and adaptation) to estimate the joint effects of storm surge and sea-level rise (SLR) for the US coast through 2100.