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HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representations, and Algorithms

HDR TRIPODS：数据科学的创新：集成随机建模、数据表示和算法

基本信息

批准号：
1934964
负责人：
Shayn Mukherjee
金额：
$ 150万
依托单位：
Duke University
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：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934964&HistoricalAwards=false
关键词：
HDR TRIPODS Innovations Data Science

项目摘要

This award supports TRIPODS@Duke Phase I, a project that will develop the foundations of data science both at Duke University and in the broader NC Research Triangle and surrounding region. A total of 25 faculty at Duke representing the disciplines of Computer Science, Electrical Engineering, Mathematics, and Statistical Science will be involved in Phase I. Activities include five semesters of workshops, with 3-4 one-week workshops each semester. These workshops will involve local and national participants and will bring experts on data science to the area. The project will support graduate students and postdoctoral trainees both in terms of education in the foundations of data science as well as in their professional development. Educational activities include the development and teaching of data science across curricula in Computer Science, Electrical and Computer Engineering, Mathematics, and Statistical Science, both at the undergraduate and graduate levels. The project will also leverage existing data science programs, including the Rhodes Information Initiative at Duke, a center for "big data" computational research and expanding opportunities for student engagement in data science; and the Statistical and Applied Mathematical Sciences Institute (SAMSI), one of the NSF/DMS-funded Mathematical Sciences Research Institutes (MSRIs), which is a partnership among Duke University, North Carolina State University (NCSU), and the University of North Carolina at Chapel Hill (UNC).The topics of the signature workshops supported by the TRIPODS@Duke Phase I project are (1) scalable inference with uncertainty, (2) causal inference, (3) neural networks, (4) complex and dynamic image and signal processing, and (5) interpretable models. These five topics all fall under three research themes that require transdisciplinary collaborations among computer scientists, electrical engineers, mathematicians, and statisticians: Theme I: Scalable algorithms with uncertainty for data science; Theme II: Data science at the human-machine interface; and Theme III: Fundamental limits of data science. The potential research innovations for the three themes that will be developed and or advanced include: For Theme I, scalable Bayesian and generalized Bayesian inference, robust optimization for uncertain inputs, and algorithm and architecture design for neural networks; for Theme II, interpretable models and algorithms, causal inference with high-dimensional complex observational data, and image and signal processing for screening and monitoring; and for Theme III, robust optimization for uncertain inputs, statistical and approximation power of deep neural network architectures, and fundamental limits of causal inference in observational studies.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.

该奖项支持TRIPODS@杜克第一阶段，该项目将在杜克大学和更广泛的NC研究三角区及周边地区发展数据科学的基础。杜克共有25名教师代表计算机科学，电气工程，数学和统计科学的学科将参与第一阶段。活动包括五个学期的研讨会，每学期有3-4个为期一周的研讨会。这些讲习班将有地方和国家参与者参加，并将把数据科学专家带到该地区。该项目将在数据科学基础教育和专业发展方面为研究生和博士后培训生提供支持。教育活动包括在计算机科学，电气和计算机工程，数学和统计科学的课程中开发和教授数据科学，无论是在本科还是研究生阶段。该项目还将利用现有的数据科学计划，包括杜克大学的罗兹信息计划，这是一个“大数据”计算研究中心，并扩大了学生参与数据科学的机会;和统计和应用数学科学研究所（SAMSI），NSF/DMS资助的数学科学研究所（MSRI）之一，这是杜克大学，北卡罗来纳州州立大学（NCSU）和查佩尔山的北卡罗来纳州大学（北卡罗来纳州）。TRIPODS@杜克第一阶段项目支持的签名研讨会的主题是（1）不确定性的可扩展推理，（2）因果推理，（3）神经网络，（4）复杂和动态图像和信号处理，（5）可解释模型。这五个主题都属于三个研究主题，需要计算机科学家，电气工程师，数学家和统计学家之间的跨学科合作：主题I：数据科学的不确定性可扩展算法;主题II：人机界面的数据科学;主题III：数据科学的基本限制。三个主题的潜在研究创新包括：主题I，可扩展贝叶斯和广义贝叶斯推理，对不确定输入的鲁棒优化，以及神经网络的算法和架构设计;主题II，可解释的模型和算法，高维复杂观测数据的因果推理，以及用于筛选和监测的图像和信号处理。对于主题III，针对不确定输入的鲁棒优化，深度神经网络架构的统计和近似能力，和观测研究中因果推理的基本限制。该项目是美国国家科学基金会利用数据革命（HDR）的一部分大创意活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。