HDR Tripods: Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS)
HDR 三脚架:德克萨斯 A
基本信息
- 批准号:1934904
- 负责人:
- 金额:$ 141.65万
- 依托单位:
- 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 2024-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Data Science is rapidly evolving as an essential interdisciplinary field, where advances often result from a combination of ideas from several disciplines. New types of data have emerged and present tremendous complexities and challenges that require a novel way of interdisciplinary thinking. The Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS) will bring together researchers from five disciplinary areas, Statistics, Electrical Engineering, Mathematics, Computer Science and Industrial Engineering, to conduct research on the foundations of data science motivated by problems arising in bioinformatics, the energy arena, power systems, and transportation systems. The Institute for Foundations of Interdisciplinary Data Science will be well-positioned to develop rigorous theories, novel methodologies, and efficient computational techniques to solve data challenges in many application domains.Modern large datasets are extremely complex and finding answers to seemingly simple questions often turns into an intractable problem. To address these challenges, FIDS will advance the foundations of data science through research on modeling complex data and developing related theory and algorithms. Development of efficient methods to identify low-dimensional structures in these high-dimensional complex data will be the key strategy to recovering high-dimensional signals with related uncertainties. Novel data-analysis models and algorithms will be developed for representation learning, information extraction, and knowledge discovery from complex data to enable better decision making. To complement the research effort, FIDS will educate and train students and postdoctoral fellows in areas at the interface of engineering, mathematics, and statistics. Targeted outreach programs will be developed to increase the pool of women and underrepresented minorities who pursue data-science careers. An external engagement program will be designed to facilitate collaborations with domain scientists and external data scientists. These programs will help to develop the intellectual foundation for a new generation of scientists poised to make novel breakthroughs in this exciting new field.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.
数据科学正在迅速发展成为一个重要的跨学科领域,其中的进步往往来自多个学科的思想组合。 新类型的数据已经出现,并提出了巨大的复杂性和挑战,需要一种新的跨学科思维方式。 德克萨斯州A M跨学科数据科学基础研究所(FIDS)将汇集来自统计学,电气工程,数学,计算机科学和工业工程五个学科领域的研究人员,对生物信息学,能源竞技场,电力系统和运输系统中出现的问题进行数据科学基础研究。跨学科数据科学基础研究所将致力于开发严谨的理论、新颖的方法和高效的计算技术,以解决许多应用领域的数据挑战。现代大型数据集极其复杂,寻找看似简单的问题的答案往往会变成一个棘手的问题。为了应对这些挑战,FIDS将通过对复杂数据建模和开发相关理论和算法的研究来推进数据科学的基础。发展有效的方法来识别这些高维复杂数据中的低维结构将是恢复具有相关不确定性的高维信号的关键策略。将开发新的数据分析模型和算法,用于从复杂数据中进行表示学习、信息提取和知识发现,以实现更好的决策。 为了补充研究工作,FIDS将在工程,数学和统计学的接口领域教育和培训学生和博士后研究员。 将制定有针对性的外展计划,以增加追求数据科学职业的女性和代表性不足的少数民族的人才库。 将设计一个外部参与计划,以促进与领域科学家和外部数据科学家的合作。 这些项目将有助于为新一代科学家奠定知识基础,使他们在这一令人兴奋的新领域取得新的突破。该项目是美国国家科学基金会利用数据革命(HDR)大创意活动的一部分。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(65)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Learning Policies with Zero or Bounded Safety Violation for Constrained MDPs
受限 MDP 的零或有限安全违规学习策略
- DOI:
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Tao Liu, Ruida Zhou
- 通讯作者:Tao Liu, Ruida Zhou
Jointly low-rank and bisparse recovery: Questions and partial answers
联合低秩和双稀疏恢复:问题和部分答案
- DOI:10.1142/s0219530519410094
- 发表时间:2020
- 期刊:
- 影响因子:2.2
- 作者:Foucart, Simon;Gribonval, Rémi;Jacques, Laurent;Rauhut, Holger
- 通讯作者:Rauhut, Holger
Low-Rank Covariance Function Estimation for Multidimensional Functional Data
多维函数数据的低秩协方差函数估计
- DOI:10.1080/01621459.2020.1820344
- 发表时间:2020
- 期刊:
- 影响因子:3.7
- 作者:Wang, Jiayi;Wong, Raymond K.;Zhang, Xiaoke
- 通讯作者:Zhang, Xiaoke
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning
- DOI:
- 发表时间:2020-11
- 期刊:
- 影响因子:0
- 作者:Akshay Mete;Rahul Singh;Xi Liu;P. Kumar
- 通讯作者:Akshay Mete;Rahul Singh;Xi Liu;P. Kumar
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits
- DOI:10.1609/aaai.v35i9.16961
- 发表时间:2020-10
- 期刊:
- 影响因子:0
- 作者:Yu-Heng Hung;Ping-Chun Hsieh;Xi Liu;P. Kumar
- 通讯作者:Yu-Heng Hung;Ping-Chun Hsieh;Xi Liu;P. Kumar
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Bani Mallick其他文献
InVA: Integrative Variational Autoencoder for Harmonization of Multi-modal Neuroimaging Data
InVA:用于协调多模态神经影像数据的综合变分自动编码器
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Bowen Lei;Rajarshi Guhaniyogi;Krishnendu Chandra;Aaron Scheffler;Bani Mallick - 通讯作者:
Bani Mallick
A Bayesian Hierarchical Model to Understand the Effect of Terrain on Wind Turbine Power Curves
用于了解地形对风力涡轮机功率曲线影响的贝叶斯分层模型
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:8.8
- 作者:
Abhinav Prakash;Se Yoon Lee;Xin Liu;Lei Liu;Bani Mallick;Yu Ding - 通讯作者:
Yu Ding
Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space–time covariance model and a Kalman filter
- DOI:
10.1016/j.spasta.2015.04.002 - 发表时间:
2015-08-01 - 期刊:
- 影响因子:
- 作者:
Denis Dreano;Bani Mallick;Ibrahim Hoteit - 通讯作者:
Ibrahim Hoteit
Bani Mallick的其他文献
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{{ truncateString('Bani Mallick', 18)}}的其他基金
ATD:Bayesian data mining approaches for Biological threat detection
ATD:用于生物威胁检测的贝叶斯数据挖掘方法
- 批准号:
0914951 - 财政年份:2009
- 资助金额:
$ 141.65万 - 项目类别:
Continuing Grant
CMG Research: Multiscale data integration using facies based hierarchical Bayesian models
CMG 研究:使用基于相的分层贝叶斯模型进行多尺度数据集成
- 批准号:
0724704 - 财政年份:2007
- 资助金额:
$ 141.65万 - 项目类别:
Standard Grant
CMG: Research on Multiscale Spatial Models for Petroleum Reservoir Mapping Using Static and Dynamic Data
CMG:利用静态和动态数据进行石油储层测绘的多尺度空间模型研究
- 批准号:
0327713 - 财政年份:2003
- 资助金额:
$ 141.65万 - 项目类别:
Continuing Grant
Bayesian Nonlinear Regression with Multivariate Linear Splines
使用多元线性样条的贝叶斯非线性回归
- 批准号:
0203215 - 财政年份:2002
- 资助金额:
$ 141.65万 - 项目类别:
Continuing Grant
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