CAREER: Statistical foundations of particle tracking and trajectory inference

职业：粒子跟踪和轨迹推断的统计基础

• 批准号: 2339829
• 负责人: Jonathan Niles-Weed
• 金额: $ 44.99万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2029-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339829&HistoricalAwards=false
• 关键词: CAREER; Statistical; foundations; particle; tracking

Many problems in human microbiology, astronomy, high-energy physics, fluid dynamics, and aeronautics involve large collections of moving "particles" with complicated dynamics. Learning how these systems work requires developing statistical procedures for estimating these dynamics on the basis of noisy observations. The goal of this research is to develop scalable, practical, and reliable methods for this task, with a particular focus on developing statistical theory for applications in cosmology, cellular biology, and machine learning. This research will also include a large outreach component based on broadening access to research opportunities for undergraduates and graduate students.The technical goals of this proposal are to develop computationally efficient estimators for multiple particle tracking in d dimensions when the particles evolve based on a known or unknown stochastic process, to develop Bayesian methods for posterior sampling based on observed trajectories, and to extend these methods to obtain minimax estimation procedures for smooth paths in the Wasserstein space of probability measures. The research also aims to develop estimators for more challenging models with the growth and interaction of particles.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.

人类微生物学，天文学，高能物理学，流体动力学和航空药物的许多问题涉及大量具有复杂动力学的移动“颗粒”集合。了解这些系统的工作方式需要开发统计程序，以根据嘈杂的观察结果估算这些动态。这项研究的目的是为此任务开发可扩展，实用和可靠的方法，特别着重于为宇宙学，蜂窝生物学和机器学习的应用开发统计理论。这项研究还将包括一个大型外展成分，基于扩大本科生和研究生的研究机会的范围。该提案的技术目标是开发粒子基于已知或未知的随机过程进化的粒子进化的计算有效估计器的多个粒子跟踪估计器，从沃斯坦的概率措施空间。该研究还旨在通过颗粒的增长和相互作用来开发更具挑战性的模型的估计器。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。