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CAREER: From Data to Knowledge and Decisions for Global-Scale Ecological Sustainability

职业：从数据到知识和全球规模生态可持续性决策

基本信息

批准号：
1749854
负责人：
Daniel Sheldon
金额：
$ 55万
依托单位：
University of Massachusetts Amherst
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749854&HistoricalAwards=false
关键词：
CAREER Data Knowledge Decisions Global

项目摘要

Emerging global data resources---for example, from citizen science projects, animal tracking devices, and earth observation instruments---hold exceptional promise for monitoring biodiversity, advancing scientific discovery, and guiding decisions to conserve Earth's natural systems. However, the full potential of these novel data resources has not been realized. The data is heterogeneous, high-dimensional, and spatiotemporal. Planning problems for conservation and sustainable development have huge numbers of states and actions, and significant modeling uncertainty. This career-development project supports an integrated program of research, teaching, and outreach to develop new algorithms to convert data into knowledge and decisions for global-scale ecological sustainability. The methods are expected to help improve scientific understanding of animal populations, increase the value of citizen science data, and contribute to development plans that balance social, economic, and ecological objectives. The technical research goals are to develop new models and algorithms for reasoning about large-scale probabilistic models and for network-based spatiotemporal planning. A new class of graphical models based on probability generating functions is proposed to cleanly model animal populations, and to provide a new and flexible class of deep generative models for general multivariate count data. A novel inference approach based on automatic differentiation will be combined with existing inference techniques to support scalable inference and learning. Algorithms that utilize causal reasoning will be developed to tease apart process from noise in citizen science data. Algorithms that exploit hierarchical structure in spatiotemporal domains will be developed to optimize multiple objectives in a highly scalable way to support sustainable development. The education plan will promote diversity, train students to conduct computer science research to solve societally relevant problems, and improve student learning outcomes through a combination of research, instructional, and mentoring activities.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.

新兴的全球数据资源--例如，来自公民科学项目、动物跟踪设备和地球观测仪器--在监测生物多样性、推进科学发现和指导保护地球自然系统的决策方面具有特殊的前景。然而，这些新的数据资源的全部潜力尚未实现。数据是异构的，高维的，时空的。保护和可持续发展的规划问题具有大量的状态和动作，以及显著的建模不确定性。这个职业发展项目支持研究，教学和推广的综合计划，以开发新的算法，将数据转换为知识和决策，以实现全球范围的生态可持续性。这些方法有望帮助提高对动物种群的科学认识，增加公民科学数据的价值，并有助于平衡社会，经济和生态目标的发展计划。技术研究目标是开发新的模型和算法，用于推理大规模概率模型和基于网络的时空规划。提出了一类新的基于概率生成函数的图形模型，以干净地对动物种群建模，并为一般多变量计数数据提供一类新的灵活的深度生成模型。一种新的推理方法的基础上自动分化将与现有的推理技术相结合，以支持可扩展的推理和学习。利用因果推理的算法将被开发出来，以将公民科学数据中的过程与噪音区分开来。将开发利用时空域层次结构的算法，以高度可扩展的方式优化多个目标，以支持可持续发展。该教育计划将促进多样性，培养学生进行计算机科学研究，以解决社会相关问题，并通过研究，教学和指导活动的组合提高学生的学习成果。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量（21）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Relaxed Marginal Consistency for Differentially Private Query Answering

差分隐私查询应答的放宽边际一致性

DOI：
发表时间：
2021
期刊：
Advances in Neural Information Processing Systems (NeurIPS
影响因子：
0
作者：
McKenna, Ryan;Pradhan, Siddhant;Sheldon, Daniel;Miklau, Gerome
通讯作者：
Miklau, Gerome

Sibling Regression for Generalized Linear Models

广义线性模型的兄弟回归

DOI：
发表时间：
2021
期刊：
Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD
影响因子：
0
作者：
Shankar, Shiv;Sheldon, Daniel
通讯作者：
Sheldon, Daniel

Kernel Interpolation with Sparse Grids