HDR TRIPODS: Collaborative Research: Foundations of Greater Data Science

HDR TRIPODS：协作研究：大数据科学的基础

基本信息

批准号：
1934962
负责人：
Mujdat Cetin
金额：
$ 81.42万
依托单位：
University of Rochester
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934962&HistoricalAwards=false
关键词：
HDR TRIPODS Collaborative Research Foundations

项目摘要

The University of Rochester and Cornell University jointly establish the Greater Data Science Cooperative Institute (GDSC). The GDSC is based on two founding tenets. The first is that enduring advances in data science require combining techniques and viewpoints across electrical engineering, mathematics, statistics, and theoretical computer science. The investigators' goal is to forge a consensus perspective on data science that transcends any individual field. The second is that data-science research must be grounded in an application domain. This helps to ensure that assumptions about the availability and quality of data are realistic, and it allows methodological results to be tested experimentally as well as theoretically. As such, the GDSC aims to consider applications in medicine and healthcare, an important application domain and one for which advances in data science can have a direct, positive impact on society. The GDSC aims to tackle foundational questions that are motivated by problems in healthcare, obtain solutions that fuse domain expertise with application-agnostic methodologies, and ultimately yield scientific advances that impact the way healthcare is provided. The GDSC aims to leverage the physical proximity of the two institutions, and the unique strengths in each of the core disciplines above and in medicine.The GDSC's cross-disciplinary research directions include: (i) Topological Data Analysis. The challenges that high-dimensional, incomplete, and noisy data present are great, but in many applications, exploiting the topological nature of the problem is possible. GDSC aims to develop new fundamental methods and theory to rigorously explore the promise of this unique approach. (ii) Data Representation. Data compression, embeddings, and dimension reduction play a fundamental role in data science. Inspired by new core challenges in biomedical imaging, genomics, and neural-spike training data, GDSC aims to develop novel source models and distortion measures, and ultimately seek a unifying theoretical framework across domains and disciplines. (iii) Network & Graph Learning. Many of the fundamental challenges in applying data science to non-homogeneous populations are best explored through a network or graph structure. GDSC aims to develop new techniques for parameter-dependent eigenvalue problems in spectral community detection, density-estimation methods on networks, and a theoretical framework for time-varying graphical models to study dynamic variable relations in time-evolving networks. (iv) Decisions, Control & Dynamic Learning. Sequential decisions are high-stakes in medicine. GDSC aims to utilize systems and control-engineering methods to improve health and disease management and develop new foundational theories and methods for label-efficient active learning and dynamic treatment regimes. (v) Diverse & Complex Modalities. Big data is complex data, and major new innovations are needed. GDSC aims to develop theoretical frameworks for inference under computational and privacy constraints and for high-dimensional data without parametric model assumptions. Text, image, and audio data present further challenges. To address such challenges, GDSC aims to explore transition systems for graph parsing of natural language and new fusion approaches for fully multimodal analysis. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.

罗切斯特大学和康奈尔大学共同建立了更大的数据科学合作社（GDSC）。 GDSC基于两个创始宗旨。首先是，数据科学的持久进展需要在电气工程，数学，统计和理论计算机科学之间结合技术和观点。研究人员的目标是对超越任何个人领域的数据科学的共识观点。第二个是数据科学研究必须基于应用领域。这有助于确保有关数据的可用性和质量的假设是现实的，并且允许在实验和理论上对方法学结果进行测试。因此，GDSC的目的是考虑医学和医疗保健中的应用，这是一个重要的应用领域，并且数据科学的进步可以对社会产生直接，积极的影响。 GDSC的目的是解决由医疗保健问题引起的基本问题，获得将领域专业知识与应用程序无关方法融合的解决方案，并最终产生影响医疗保健方式的科学进步。 GDSC旨在利用这两个机构的物理接近，以及上述每个核心学科的独特优势。GDSC的跨学科研究方向包括：（i）拓扑数据分析。存在的高维，不完整和嘈杂数据的挑战很棒，但是在许多应用中，利用问题的拓扑性质是可能的。 GDSC旨在开发新的基本方法和理论，以严格探讨这种独特方法的希望。（ii）数据表示。数据压缩，嵌入和缩小尺寸在数据科学中起着基本作用。受生物医学成像，基因组学和神经尖峰训练数据的新核心挑战的启发，GDSC旨在开发新颖的源模型和失真度量，并最终寻求跨领域和学科的统一理论框架。（iii）网络和图形学习。最好通过网络或图形结构来探索许多将数据科学应用于非殖民群体的基本挑战。 GDSC旨在开发新技术，以依赖参数依赖的特征值问题，在光谱群落检测中，网络上的密度估计方法以及时间变化的图形模型的理论框架，以研究时间发展网络中的动态可变关系。（iv）决策，控制和动态学习。顺序决定是医学的高风险。 GDSC旨在利用系统和控制工程方法来改善健康和疾病管理，并为标签有效的主动学习和动态治疗方案开发新的基础理论和方法。（v）多样化和复杂的方式。大数据是复杂的数据，需要重大的新创新。 GDSC的目的是开发用于计算和隐私约束下推断的理论框架以及没有参数模型假设的高维数据。文本，图像和音频数据带来了进一步的挑战。为了应对此类挑战，GDSC旨在探索自然语言的图形解析和新的融合方法的过渡系统，以进行完全多模态分析。该项目是国家科学基金会利用数据革命（HDR）的大创意活动的一部分。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来支持。