CAREER: Data-Starved Inference on Point Processes
职业:点过程上的数据匮乏推理
基本信息
- 批准号:0643947
- 负责人:
- 金额:$ 40万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2007
- 资助国家:美国
- 起止时间:2007-02-15 至 2013-01-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
On the surface, analyzing the 2003 blackout in New York City, evaluating transportation systems for bottlenecks and emergency preparedness, and imaging a cell's genetic activity are very different challenges. However, these and many other important problems are all characterized by limited measurements of discrete events, such as a cascading series of system failure events, vehicles passing a small number of checkpoints in a transportation network, or tiny pulses of light emitted by a gene-triggered dye. This research is focused on the development of innovative theoretical and computational tools based on sophisticated statistical analyses for extracting meaningful information from small numbers of indirectly observed discrete events. Several educational programs are tightly coupled with these research pursuits, including the development of novel hands-on student activities focused on deriving information from indirect measurements.These programs are designed to stimulate interest in engineering disciplines through real-world examples and ultimately improve technical literacy. Data-starved inference on high-dimensional point processes encompasses a wide variety of fundamental theoretical challenges coupled with many critical applications, including fluorescence microscopy, network traffic analysis, astrophysics, and spectroscopy. This research comprises the development of a theoretical and methodological framework for analyzing and guiding the design of high-dimensional, data-starved, and complex event-based systems which may be dynamically changing, marked with auxiliary feature vectors, or supported on irregular topologies. The educational aspects of this work are tightly integrated with the research activities and help further advance the understanding of signal processing challenges and tools among both engineers and society at large. In particular, the investigators are designing outreach activities for primary and secondary school students and developing undergraduate- and graduate-level courses on multidimensional and statistical signal processing.
从表面上看,分析2003年纽约市的大停电,评估交通系统的瓶颈和应急准备,以及成像细胞的遗传活动是非常不同的挑战。然而,这些和许多其他重要问题的特征都是对离散事件的有限测量,例如一连串的系统故障事件,交通网络中通过少量检查站的车辆,或基因触发染料发出的微小光脉冲。这项研究的重点是开发基于复杂统计分析的创新理论和计算工具,从少量间接观察到的离散事件中提取有意义的信息。一些教育项目与这些研究活动紧密结合,包括开发新颖的动手学生活动,专注于从间接测量中获取信息。这些项目旨在通过真实世界的例子激发对工程学科的兴趣,并最终提高技术素养。高维点过程的数据匮乏推理包括各种各样的基本理论挑战,以及许多关键应用,包括荧光显微镜,网络流量分析,天体物理学和光谱学。这项研究包括一个理论和方法框架的发展,用于分析和指导设计的高维,数据匮乏,复杂的基于事件的系统,可能是动态变化的,标记有辅助特征向量,或支持不规则的拓扑结构。这项工作的教育方面与研究活动紧密结合,有助于进一步提高工程师和整个社会对信号处理挑战和工具的理解。特别是,调查人员正在为中小学生设计推广活动,并编制关于多维和统计信号处理的本科生和研究生课程。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Rebecca Willett其他文献
Multi-Frequency Progressive Refinement for Learned Inverse Scattering
学习逆散射的多频率渐进细化
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Owen Melia;Olivia Tsang;Vasileios Charisopoulos;Y. Khoo;Jeremy Hoskins;Rebecca Willett - 通讯作者:
Rebecca Willett
Stability via resampling: statistical problems beyond the real line
通过重采样实现稳定性:超出实线的统计问题
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Jake A. Soloff;Rina Foygel Barber;Rebecca Willett - 通讯作者:
Rebecca Willett
SUPERNOVA EJECTA IN THE YOUNGEST GALACTIC SUPERNOVA REMNANT G1.9+0.3
最年轻的银河系超新星遗迹 G1.9 0.3 中的超新星喷射物
- DOI:
10.1088/2041-8205/771/1/l9 - 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
K. Borkowski;S. Reynolds;U. Hwang;D. Green;R. Petre;Kalyani Krishnamurthy;Rebecca Willett - 通讯作者:
Rebecca Willett
RADIOACTIVE SCANDIUM IN THE YOUNGEST GALACTIC SUPERNOVA REMNANT G1.9+0.3
最年轻的银河超新星遗迹 G1.9 0.3 中的放射性钪
- DOI:
10.1088/2041-8205/724/2/l161 - 发表时间:
2010 - 期刊:
- 影响因子:0
- 作者:
K. Borkowski;S. Reynolds;D. Green;U. Hwang;R. Petre;Kalyani Krishnamurthy;Rebecca Willett - 通讯作者:
Rebecca Willett
Rebecca Willett的其他文献
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{{ truncateString('Rebecca Willett', 18)}}的其他基金
NSF Student Travel Grant for 2022 UChicago AI+Science Summer School (UChicago AI+Sci SS)
2022 年芝加哥大学人工智能科学暑期学校 (UChicago AI Sci SS) NSF 学生旅费补助
- 批准号:
2229623 - 财政年份:2022
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
TRIPODS: Institute for Foundations of Data Science
TRIPODS:数据科学研究所
- 批准号:
2023109 - 财政年份:2020
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
Collaborative Research: Physics-Based Machine Learning for Sub-Seasonal Climate Forecasting
合作研究:基于物理的机器学习用于次季节气候预测
- 批准号:
1934637 - 财政年份:2019
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
ATD: Collaborative Research: Automatic, Adaptive Detection and Description of Change in Time-Lapse Imagery
ATD:协作研究:延时图像变化的自动、自适应检测和描述
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1925101 - 财政年份:2019
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
TRIPODS+X:RES: Collaborative Research: Data Science Frontiers in Climate Science
TRIPODS X:RES:合作研究:气候科学中的数据科学前沿
- 批准号:
1839338 - 财政年份:2018
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
TRIPODS+X:RES: Collaborative Research: Data Science Frontiers in Climate Science
TRIPODS X:RES:合作研究:气候科学中的数据科学前沿
- 批准号:
1930049 - 财政年份:2018
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
CIF: Small: Sparsity and Scarcity in High-Dimensional Point Processes
CIF:小:高维点过程中的稀疏性和稀缺性
- 批准号:
1319927 - 财政年份:2013
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
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