CAREER: Guided Sensing

在许多与发现、检测和诊断相关的复杂问题中，研究人员和从业人员都不断面临着这样的问题：接下来我应该收集哪些数据？当数据收集的可能性是压倒性的，实验或测量是昂贵的或耗时的，这个问题变得更加critical.This研究调查引导传感算法，它使有关收集下一个测量的建议，理解领域专家作出最终决定。受应急响应和基于细胞的高通量分析应用的启发，这项工作开发了新的方法，用于指导传感，占时间和基于任务的约束，丢失的数据，环境噪声以及人为错误。首先，它概括了经典的基于查询的学习算法是强大的噪音，输入和输出的设计，以匹配用户？需求为了实现这一点，贪婪决策树算法的设计方面的新的性能指标，反映特定任务的目标和约束。其次，这项工作开发了交互式的，非参数的统计匹配方法，在数据融合的一个基本问题。该方法是基于新的方法，非参数聚类与缺失数据，和无监督的序贯实验设计。

In many complex problems related to discovery, detection, and diagnosis, researchers and practitioners alike are continually faced with the question ?What data should I gather next?? When the possibilities for data collection are overwhelming, and experiments or measurements are costly or time-consuming, this question becomes all the more critical.This research investigates guided sensing algorithms, which make recommendations about the next measurements to gather, with the understanding that a domain expert makes the final decision. Motivated by applications in emergency response and high-throughput cell-based analysis, this work develops new methods for guided sensing that account for temporal and task-based constraints, missing data, and environmental noise as well as human error.The research makes two primary technical contributions. First, it generalizes classical query-based learning algorithms to be robust to noise, with input and output designed to match users? needs. To accomplish this, greedy decision tree algorithms are designed with respect to new performance measures that reflect task-specific objectives and constraints. Second, this work develops interactive, nonparametric methods for statistical matching, a fundamental problem in data fusion. The approach is grounded in new methods for nonparametric clustering with missing data, and for unsupervised sequential experimental design.