CAREER: Structured Nonlinear Estimation via Message Passing: Theory and Applications

当今工程和科学面临的一个根本挑战是，系统包含大量相互关联的组件，这些组件具有复杂的相互作用。 例子包括通信和传感器网络、高维医学图像或生物系统，例如响应于大量刺激的大量互连尖峰神经元。 图形模型提供了一个概率框架来建模这样的系统，当代的消息传递算法通过将较大系统上的问题分解为较小的问题来实现计算上可行的操作。 这项研究开发了一种更广泛的方法和新算法，以解决更大类别的更复杂的非线性互联系统，具有巨大的技术影响潜力。 为了更广泛的传播，这是再加上教育举措，包括开发课程，结合现代应用背景下的信号处理，机器学习和统计学的观点。 开源代码库将促进学生，教育工作者和行业的跨学科研究。这项研究将高维图形模型的力量与随机系统理论的最新进展相结合，以解决比传统消息传递或线性方法更广泛的问题。 研究人员解决了结构化非线性系统的可扩展估计和模型推理的关键差距，并开发了强大的通用算法来解决核心问题。 四个主要目标解决这个更广泛的目标方面：（一）系统的一般方法表示系统的特点是任意互连的线性和非线性组件;（二）计算可扩展的消息传递算法的估计;（三）严格量化的高维性能;（四）验证的方法对真实的数据，包括神经系统识别。 这些研究方向极大地扩展了统计估计技术的范围，并为当今大数据技术的大规模信号处理问题提供了严格的方法。

A fundamental challenge in engineering and science today is that systems contain tremendous numbers of interconnected components with complex interactions. Examples include communication and sensor networks, high-dimensional medical images, or biological systems such as vast sets of interconnected spiking neurons responding to a large array of stimuli. Graphical models provide a probabilistic framework for modeling such systems, and contemporary message-passing algorithms lead to computationally feasible operations by decomposing problems on larger systems into smaller ones. This research develops a broader methodology and new algorithms to address larger classes of more complex nonlinear interconnected systems with potential for great technological impact. For wider dissemination, this is coupled with educational initiatives including developing courses combining perspectives in signal processing, machine learning, and statistics in the context of modern applications. An open-source code base will foster cross-disciplinary research in students, educators, and industry.This research combines the power of high-dimensional graphical models with recent advances in random systems theory to tackle a much wider scope of problems than traditional message-passing or linear methods allow. The investigator addresses the key gaps in scalable estimation and model inference for structured nonlinear systems and develops powerful general algorithms for solving core problems. Four main objectives address aspects of this broader goal: (i) systematic general methods for representing systems characterized by arbitrary interconnections of linear and nonlinear components; (ii) computationally scalable message-passing algorithms for estimation; (iii) rigorous quantification of high-dimensional performance; and (iv) validation of the methods on real data, including neurological system identification. These research thrusts greatly expand the scope of statistical estimation techniques and provide a rigorous approach to large-scale signal processing problems underlying the big data technology of today.