CAREER: Probabilistic Models for Spatiotemporal Data with Applications to Dynamic Brain Connectivity

概率模型是处理复杂时空数据最有前途的工具之一。然而，将这一承诺转化为实际影响需要易于部署的工具，以适当解决现有的障碍。该项目开发了新的工具，用于使用时空数据进行准确和可扩展的概率机器学习。此外，该方法的动机是从人脑成像数据映射动态脑连接的应用程序。动态脑连接的重要性在于它描述了神经信息处理机制，沿着潜在的变革性应用，以理解和治疗神经和神经精神疾病。该项目将开发用于估计大脑连接的新技术，并将这些方法应用于解释认知和行为个体间差异的神经科学任务。 该项目将包括时空数据概率模型的课程编制。该项目还计划吸收代表性不足群体的研究生参与。该项目通过解决建模，可扩展性和减轻数据偏差的基本挑战，为高维时空数据建模创造了一个变革性的新方向。第一个挑战是建模，这是指现有时空模型的不灵活假设-导致拟合不足。为此，该项目开发了捕获结构化变异的模块化概率模型。 另一个紧迫的挑战是这种概率模型的推理和学习的计算可扩展性。该项目通过开发具有性能保证的可扩展近似推理的原则性样本选择方法来解决可扩展性问题。 第三个挑战是数据偏差，这是因为来自单一来源的数据往往不具有统计代表性。因此，使用单源数据拟合的模型具有不一致和不可重现的结果。该项目通过使用新颖的联邦学习来共享估计而不需要直接数据共享来组合多个来源的数据，从而解决数据偏差问题。除了为这些方向开发算法和理论框架外，该项目还将构建和发布开放软件。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Probabilistic models are among the most promising tools for complex spatiotemporal data. However, transforming this promise to practical impact requires easy-to-deploy tools that appropriately address existing roadblocks. This project develops new tools for accurate and scalable probabilistic machine learning with spatiotemporal data. Furthermore, the approach is motivated by applications to mapping dynamic brain connectivity from human brain imaging data. The importance of dynamic brain connectivity lies in its description of neural information processing mechanisms, along with potentially transformative applications to understanding and treating neurological and neuropsychiatric disorders. This project will develop new techniques for estimating brain connectivity and apply these methods to the neuroscientific tasks of explaining inter-individual differences in cognition and behavior. This project will include curriculum development on probabilistic models for spatiotemporal data. This project also plans to involve participation by graduate students from underrepresented groups. This project creates a transformative new direction for modeling high-dimensional spatiotemporal data by addressing the fundamental challenges of modeling, scalability, and mitigating data biases. The first challenge is modeling, which refers to the inflexible assumptions of existing spatiotemporal models -- leading to under-fitting. To this end, this project develops modular probabilistic models that capture structured variability. Another pressing challenge is the computational scalability of inference and learning for such probabilistic models. This project tackles scalability by developing principled sample-selection methods for scalable approximate inference with performance guarantees. A third challenge is data bias, which occurs because data from a single source is often not statistically representative. Thus, models fit using single-source data have inconsistent and non-reproducible results. This project addresses data bias by combining data across multiple sources using novel federated learning for shared estimation without requiring direct data sharing. In addition to developing the algorithmic and theoretical frameworks for these directions, this project will also build and release open software.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.