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这样的问题：接下来我应该收集什么资料？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，这个问题变得更加关键。本研究研究了引导传感算法，该算法根据领域专家做出最终决定的理解，对下一个要收集的测量提出建议。在应急响应和基于高通量细胞的分析应用的激励下，这项工作开发了指导传感的新方法，该方法考虑了时间和任务约束、缺失数据、环境噪声以及人为错误。这项研究有两个主要的技术贡献。首先，它推广了经典的基于查询的学习算法，使其对噪声具有鲁棒性，输入和输出的设计与用户匹配。的需求。为了实现这一点，贪婪决策树算法被设计为反映任务特定目标和约束的新性能度量。其次，本工作开发了用于统计匹配的交互式非参数方法，这是数据融合的一个基本问题。该方法基于缺失数据的非参数聚类和无监督顺序实验设计的新方法。

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.