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

预测生物多样性，即物种丰度，以应对气候变化是环境变化研究的一个目标。尽管最近在了解生物多样性和气候方面取得了有价值的进展，但目前的掌握是有限的。有两个公认的障碍：首先，由于底层过程的复杂性，用于理解和预测的现有模型（在计算上）是不可伸缩的。其次，粗尺度环境模型无法捕捉控制生物多样性的物种之间的相互作用，而基于细尺度、短期观测的模型无法做出长期预测。该项目旨在开发一种预测框架，将物种相互作用的大尺度模式数据与精细尺度数据连贯地结合起来，并在计算上可扩展。它侧重于地理尺度上的预测，并利用地理尺度上的数据来更好地理解物种相互作用发生的尺度。目标是开发一个多尺度建模框架，并设计算法，使环境模型在计算上可扩展。该方法依赖于算法和统计技术的强大相互作用。统计推理带来了空间和时间上的随机建模复杂性，从而改进了过程的表征和充分推理的可能性。复杂的算法使模型和过程可扩展，并在准确性和效率之间提供权衡。该项目借鉴了计算机科学和统计学的广泛主题，包括几何算法、近似算法、贝叶斯框架内的层次规范和时空过程建模。在提议的原型示例中所涉及的问题领域表明了计算机科学和统计学中更广泛适用的后续挑战。这些包括维护/更新分布和摘要、动态算法、数据驱动算法、随机优化、评估推理中的不确定性和多尺度非线性相互作用。为了优化模型的整体性能，需要在冲突目标之间获得权衡的技术。

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.