Flexible Statistical Modeling

该项目为解决医学和科学中的重要应用问题开发了新的统计方法。在个性化医疗方面，PI将专注于根据患者的人口统计数据和临床病史，利用过去患者的大量经验，预测特定治疗是否适合患者。生物学家试图了解细胞中染色体的折叠模式，这是了解其功能的关键因素。在第二个项目中，PI将开发新的曲线拟合方法，从对这种三维结构的间接和噪声测量中学习这些折叠模式。生态学家试图了解吸引某些物种的环境特征，以及共同栖息在环境中的物种的共同方面，作为物种生存、病虫害控制和疾病预防的关键组成部分。在第三个项目中，PI将开发基于特定地点调查的方法，这些方法可以扩展到极大的物种种群(如细菌和昆虫)。该项目还为研究生提供了研究培训机会。该项目开发了验证方法，以从一系列模型中进行选择，以估计异质治疗效果，尽管在观察数据中没有直接测量治疗效果的事实。该项目开发了自适应最近邻匹配技术，以构建每个验证点的比较集。利用高维染色体接触图，这位PI计划利用他早期在主曲线上的工作来模拟染色体的三维折叠结构。这相当于利用有关三维解决方案的局部结构的辅助信息进行度量缩放。广义线性潜变量模型是模拟物种分布的常用模型(通常用于计数的泊松模型和用于存在/不存在的二项式模型)，但当物种和/或位置的数量很大时，它们就会停滞不前。PI计划调整矩阵完成的早期工作，以开发交替的最大似然拟合算法，以将这些方法扩展到极大的人群。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project develops new statistical methodology for solving important applied problems in medicine and science. In personalized medicine, the PI will focus on the prediction of whether particular treatments are suitable for a patient based on their demographics and clinical history, using vast troves of past patient experiences. Biologists seek to understand the folding patterns of chromosomes in cells, a key ingredient in understanding their function. In a second project, the PI will develop novel curve-fitting methods to learn these folding patterns from indirect and noisy measurements of this three dimensional structure. Ecologists try to learn the characteristics of environments that attract certain species, as well as shared aspects of species that coinhabit environments, as a critical component in species survival, pest control and disease prevention. In a third project, the PI will develop methods that can scale to extremely large species populations (such as bacteria and insects) based on site-specific surveys. The project also provides research training opportunities for graduate students. The project develops validation methods to select from a collection of models for estimating heterogenous treatment effects, despite the fact that in observational data there are no direct measurements of the treatment effect. The project develops adaptive nearest-neighbor matching techniques to construct a comparison set for each validation point. With high-dimensional chromosomal contact maps, the PI plans to draw on his early work on principal curves to model the three-dimensional folding structure of chromosomes. This amounts to metric scaling with side information on the local structure of the three-dimensional solution. Generalized linear latent-variable models are popular for modeling species distributions (usually Poisson models for counts, and binomial models for presence/absence), but they grindto a halt when the number of species and/or locations is very large. The PI plans to adapt earlier work on matrix completion to develop alternating maximum-likelihood fitting algorithms to scale these methods to extremely large populations.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.