HDR Tripods: Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS)
HDR Tripods: Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS)
批准号:
1934904
负责人:
Bani Mallick
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
项目状态:
已结题
起止时间:
2019-10-01 至 2024-09-30
中文摘要
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英文摘要
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.
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DOI:
10.1016/j.jco.2020.101503
发表时间:
2020
期刊:
Journal of Complexity
影响因子:
1.7
作者:
[Foucart, Simon]
通讯作者:
Foucart, Simon
DOI:
10.1371/journal.pone.0236860
发表时间:
2020-07-29
期刊:
PLOS ONE
影响因子:
3.7
作者:
[Lee, Se Yoon, Lei, Bowen, Mallick, Bani]
通讯作者:
Mallick, Bani
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition
VFDS:贝叶斯神经网络中的变分预见动态选择,用于高效的人类活动识别
DOI:
--
发表时间:
2022
期刊:
The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022
影响因子:
--
作者:
[Randy Ardywibowo, Shahin Boluki]
通讯作者:
Randy Ardywibowo, Shahin Boluki
Single and Multiple Change-Point Detection with Differential Privacy
具有差分隐私的单个和多个变化点检测
DOI:
--
发表时间:
2021
期刊:
Journal of machine learning research
影响因子:
6
作者:
[Zhang, Wanrong, Krehbiel, Sara, Tuo, Rei, Mei, Yajun, Cummings, Rachel]
通讯作者:
Cummings, Rachel
Tensor Linear Regression: Degeneracy and Solution
张量线性回归:简并性和解
DOI:
10.1109/access.2021.3049494
发表时间:
2021-01-01
期刊:
IEEE ACCESS
影响因子:
3.9
作者:
[Zhou,Ya, Wong,Raymond K. W., He,Kejun]
通讯作者:
He,Kejun
共 54 条
ATD:Bayesian data mining approaches for Biological threat detection
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批准号:0914951
-
项目类别:Continuing Grant
-
资助金额:$83.5万
-
财政年份:2009
-
负责人:Bani Mallick
-
依托单位:
CMG Research: Multiscale data integration using facies based hierarchical Bayesian models
-
批准号:0724704
-
项目类别:Standard Grant
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资助金额:$65.0万
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财政年份:2007
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负责人:Bani Mallick
-
依托单位:
CMG: Research on Multiscale Spatial Models for Petroleum Reservoir Mapping Using Static and Dynamic Data
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批准号:0327713
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项目类别:Continuing Grant
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资助金额:$55.3万
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财政年份:2003
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负责人:Bani Mallick
-
依托单位:
Bayesian Nonlinear Regression with Multivariate Linear Splines
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批准号:0203215
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项目类别:Continuing Grant
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资助金额:$15.91万
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财政年份:2002
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负责人:Bani Mallick
-
依托单位:
海外基金