International Conference on Mathematics of Data Science
国际数据科学数学会议
基本信息
- 批准号:1839457
- 负责人:
- 金额:$ 1.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-11-01 至 2019-10-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Data science is an emerging interdisciplinary field of science and technology. It aims at developing theory, methods and techniques for extraction of useful knowledge or insights from raw data in various structured or unstructured forms such as signal, radar, sounds, images, videos and texts to make smart decisions. Data science employs theories and methods drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. Mathematics plays an indispensable role in data science. To bring together active researchers in various fields related to data science and practitioners in industry to identify mathematical and statistical challenges in data science, the International Conference on Mathematics of Data Science is being held on the campus of Old Dominion University on November 3-4, 2018. The conference website is http://icmds2018.org. The conference invited speakers are internationally known researchers in the field of data science and will address critical mathematical issues of the field. The conference will promote research collaboration among different areas, cultivate research partnership between academia and industry and, in particular, encourage young talents to work in the field of data science. The funds will solely be used to support junior researchers and graduate students in related fields at US universities and research institutions to attend the conference. This supports the recruitment of young talent to the field of data science and preparation of the next generation researchers to meet the scientific challenges in the big data era. Special efforts are made to recruit graduate students from underrepresented groups such as African American students and female students for the conference participants.The conference will cover mathematical topics crucial to data science. In the field of data science, mathematics has provided functional spaces to represent data sets, approximation approaches to characterize similarity and difference of data sets, optimization methods to extract information from raw data, and analytical, geometrical tools to describe insightful relationships among various concepts in data and their statistical analysis. Further development of data science demands that mathematics play a leading role. For example, it is not yet fully understood that why deep learning is very efficient for certain applications while less efficient in other scenarios. This requires mathematical understanding of the fundamental issues in deep learning. All these issues will be the focus of the conference. Specifically, its scope covers sparse representation of big data sets, functional spaces suitable for big data analysis, mathematical foundation of machine learning, signal image processing, statistical analysis for big data, convex or non-convex sparse optimization for data analysis, scalable algorithms for big data and applications of data science. Scientific and societal broader impacts of this project lie in the aspects that the conference will promote interaction of mathematics, statistics, computer science, engineering and industrial applications, which support the interdisciplinary field of data science, and it will provide a platform for young scholars to learn about and discuss challenging mathematical issues in the field.Website: https://sites.wp.odu.edu/icmds2018/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.
数据科学是一门新兴的跨学科科学技术领域。它旨在发展理论、方法和技术,从各种结构化或非结构化形式的原始数据(如信号、雷达、声音、图像、视频和文本)中提取有用的知识或见解,以做出明智的决策。数据科学采用的理论和方法来自数学、统计学、信息科学和计算机科学等广泛领域的许多领域。数学在数据科学中扮演着不可或缺的角色。为了汇集与数据科学相关的各个领域的活跃研究人员和行业从业者,以确定数据科学中的数学和统计挑战,国际数据科学数学会议将于2018年11月3日至4日在Old Dominion University校园举行。会议网站是http://icmds2018.org。会议邀请的演讲者是数据科学领域的国际知名研究人员,并将讨论该领域的关键数学问题。会议将促进不同领域的研究合作,培育学术界与产业界的研究伙伴关系,特别是鼓励年青人才在数据科学领域工作。这些资金将仅用于支持美国大学和研究机构相关领域的初级研究人员和研究生参加会议。这为数据科学领域招募年轻人才提供了支持,并为下一代研究人员做好准备,以迎接大数据时代的科学挑战。特别努力从代表性不足的群体中招收研究生,如非洲裔美国学生和女学生作为会议参与者。会议将涵盖对数据科学至关重要的数学主题。在数据科学领域,数学提供了表示数据集的功能空间,描述数据集相似性和差异性的近似方法,从原始数据中提取信息的优化方法,以及描述数据及其统计分析中各种概念之间深刻关系的分析几何工具。数据科学的进一步发展要求数学发挥主导作用。例如,人们还没有完全理解为什么深度学习在某些应用中非常有效,而在其他场景中效率较低。这需要对深度学习中的基本问题有数学上的理解。所有这些问题都将是这次会议的焦点。具体来说,它的范围包括大数据集的稀疏表示、适合大数据分析的功能空间、机器学习的数学基础、信号图像处理、大数据的统计分析、数据分析的凸或非凸稀疏优化、大数据的可扩展算法以及数据科学的应用。该项目的更广泛的科学和社会影响在于会议将促进数学,统计学,计算机科学,工程和工业应用的互动,从而支持数据科学的跨学科领域,并将为年轻学者提供一个学习和讨论该领域具有挑战性的数学问题的平台。网站:https://sites.wp.odu.edu/icmds2018/This该奖项反映了美国国家科学基金会的法定使命,并通过基金会的知识价值和更广泛的影响审查标准进行评估,认为值得支持。
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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Yuesheng Xu其他文献
Multiplicative Noise Removal: Nonlocal Low-Rank Model and Its Proximal Alternating Reweighted Minimization Algorithm
乘性噪声消除:非局部低秩模型及其近端交替重加权最小化算法
- DOI:
10.1137/20m1313167 - 发表时间:
2020-01 - 期刊:
- 影响因子:0
- 作者:
Xiaoxia Liu;Yuesheng Xu;Jian Lu;Lixin Shen;Chen Xu - 通讯作者:
Chen Xu
A deblurring/denoising corrected scintigraphic planar image reconstruction model for targeted alpha therapy
用于靶向α治疗的去模糊/去噪校正闪烁扫描平面图像重建模型
- DOI:
10.1117/12.2584736 - 发表时间:
2021-02 - 期刊:
- 影响因子:0
- 作者:
Lisa Bodei;Ida Häggström;Matthew K. Maroun;Andrzej Krol;Yuesheng Xu;Joseph O'Donoghue;Howard Gifford;Charles Ross Schmidtlein - 通讯作者:
Charles Ross Schmidtlein
Fixed-point proximity algorithms solving an incomplete Fourier transform model for seismic wavefield modeling
定点邻近算法求解地震波场建模的不完全傅立叶变换模型
- DOI:
10.1016/j.cam.2020.113208 - 发表时间:
2021-03 - 期刊:
- 影响因子:0
- 作者:
Yuesheng Xu;Lixin Shen;Tingting Wu - 通讯作者:
Tingting Wu
On computing with the Hilbert spline transform
关于希尔伯特样条变换的计算
- DOI:
10.1007/s10444-011-9252-x - 发表时间:
2013-04 - 期刊:
- 影响因子:1.7
- 作者:
C. A. Micchelli;Yuesheng Xu;Bo Yu - 通讯作者:
Bo Yu
Constrained best approximation in Hilbert space III. Applications ton-convex functions
希尔伯特空间 III 中的约束最佳近似。
- DOI:
10.1007/bf02433049 - 发表时间:
1996 - 期刊:
- 影响因子:2.7
- 作者:
F. Deutsch;V. Ubhaya;J. Ward;Yuesheng Xu - 通讯作者:
Yuesheng Xu
Yuesheng Xu的其他文献
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{{ truncateString('Yuesheng Xu', 18)}}的其他基金
Collaborative Research: Sparse Optimization for Machine Learning and Image/Signal Processing
协作研究:机器学习和图像/信号处理的稀疏优化
- 批准号:
2208386 - 财政年份:2022
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
Collaborative Research: Sparse Optimization in Large Scale Data Processing: A Multiscale Proximity Approach
协作研究:大规模数据处理中的稀疏优化:多尺度邻近方法
- 批准号:
1912958 - 财政年份:2019
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
Collaborative Research: An Efficient Programming Model for HPC Applications on Next-Generation High-end Parallel Machines
协作研究:下一代高端并行机上 HPC 应用的高效编程模型
- 批准号:
0833152 - 财政年份:2008
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
Multiscale Total Variation Methods for Integral Equation Models in Image Processing
图像处理中积分方程模型的多尺度全变分法
- 批准号:
0712827 - 财政年份:2007
- 资助金额:
$ 1.5万 - 项目类别:
Continuing Grant
ITR: Estimation, Approximation and Computation in Learning Theory
ITR:学习理论中的估计、近似和计算
- 批准号:
0407476 - 财政年份:2003
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
ITR: Estimation, Approximation and Computation in Learning Theory
ITR:学习理论中的估计、近似和计算
- 批准号:
0312113 - 财政年份:2003
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
Adaptive Wavelet Methods for Boundary Integral Equations
边界积分方程的自适应小波方法
- 批准号:
0296024 - 财政年份:2001
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
Adaptive Wavelet Methods for Boundary Integral Equations
边界积分方程的自适应小波方法
- 批准号:
9973427 - 财政年份:1999
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
U.S.-China Cooperative Research: Symposium on Computational Mathematics, Guangzhou, China, August 1997
美中合作研究:计算数学研讨会,中国广州,1997 年 8 月
- 批准号:
9604916 - 财政年份:1997
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
Mathematical Sciences: Construction of Wavelets on Finite Domans and Applications to Boundary Integral Equations
数学科学:有限域上的小波构造及其在边界积分方程中的应用
- 批准号:
9504780 - 财政年份:1995
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
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