CDI-Type II: Integrating Algorithmic and Stochastic Modeling Techniques for Environmental Prediction

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Predicting biodiversity, i.e., abundance of species, in response to climate change is a goal of environmental change research. Despite recent valuable advances in understanding biodiversity and climate, the current grasp is limited. There are two widely recognized obstacles: first, because of the complexity of the underlying processes, the existing models intended for understanding and prediction are not (computationally) scalable. Second, the coarse-scale environment models fail to capture interactions among species, which control biodiversity, and the models based on fine-scale, short-term observations are unable to make long-term predictions. This project aims to develop a prediction framework that coherently combines broad-scale pattern data with fine-scale data on species interactions and that is computationally scalable. It focuses on prediction at the geographic scale and in using geographic-scale data to better understanding at the scales where species interactions occur.The goal is to develop a multiscale modeling framework and to design algorithms that make environmental models computationally scalable. The approach hinges upon strong interplay of algorithmic and statistical techniques. Statistical inference brings stochastic modeling sophistication in space and time, yielding improved characterization of the process and the possibility of full inference. Sophisticated algorithms make models and processes scalable and provide trade-offs between accuracy and efficiency. The project draws on a wide range of topics in computer science and statistics, including geometric algorithms, approximation algorithms, hierarchical specifications within a Bayesian framework, and space-time process modeling. The problem areas address in the proposed prototypical example indicate more broadly applicable consequential challenges for both computer science and statistics. These include maintaining/updating distributions and summaries, dynamic algorithms, data driven algorithms, stochastic optimization, and assessing uncertainty and multi-scale nonlinear interactions in inference. Techniques for obtaining trade-offs between conflicting goals are needed in order to optimize the overall performance of the model.