HDR Institute: HARP- Harnessing Data and Model Revolution in the Polar Regions
HDR Institute: HARP- Harnessing Data and Model Revolution in the Polar Regions
批准号:
2118285
负责人:
Vandana Janeja
金额:
$1300.0万
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-01-01 至 2026-12-31
中文摘要
点击翻译按钮获取中文摘要
英文摘要
Climate-change induced loss of polar ice sheets impacts many lives and increases coastal flooding by rising sea level and affecting ocean circulation. However, it remains difficult to accurately predict how quickly the ice sheets will continue to shrink. In particular, we are still challenged by a limited understanding of transdisciplinary processes that determine ice sheet change, such as the role of subglacial topography and ice-atmosphere-ocean interactions. Timely investment in machine learning and data intensive research can revolutionize the way that scientists currently answer questions related to ice dynamics. This HDR Institute serves as a research hub where experts in data science, Arctic and Antarctic science, and cyberinfrastructure in academia, government, and private sectors come together to develop transformative and integrative data science solutions to reduce uncertainties in projecting future sea-level rise and climate change. i-HARP researchers investigate the potential of novel physics-aware data science and machine learning approaches to address national priorities and challenges on Navigating the New Arctic, climate change, and sea-level rise.The HDR Institute aims to harness massive heterogeneous, noisy, and discontinuous data in space and time and integrate data with numerical and physical models. Researchers at i-HARP are investigating novel data science techniques including deep generative adversarial networks, graph neural networks, meta learning, hybrid networks, physics-informed machine learning, causal artificial intelligence, data assimilation, spatiotemporal deep learning, and scalable algorithms. Due to the fundamental nature of data science problems that i-HARP addresses, the solutions can be translated to other disciplines such as remote sensing, medicine, and autonomous driving. Moreover, the convergence team champions multiple clusters of research-integrated educational initiatives, with a specific focus on facilitating cross-disciplinary collaborations, training next-generation multi-disciplinary researchers and engaging the public in scientific inquiry as related to climate change and data science. In partnership with related communities, i-HARP designs curricula, and offers hands-on community workshops, lecture series, conference tutorials, and training. i-HARP engages students from underrepresented minority groups by leveraging several existing organizations for underrepresented minorities.This project is part of the National Science Foundation's Big Idea activities in Harnessing the Data Revolution (HDR). This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Section for Antarctic Sciences and the Section for Arctic Sciences within the NSF Office of Polar Programs.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.
期刊论文(16)
专著(0)
科研奖励(0)
会议论文
登录
查看更多内容
Mobile Augmented Reality System for Emergency Response
用于应急响应的移动增强现实系统
DOI:
--
发表时间:
2023
期刊:
Management and Applications (SERA 2023
影响因子:
--
作者:
[Sharma, S]
通讯作者:
Sharma, S
DOI:
--
发表时间:
2023
期刊:
IEEE International Geoscience and Remote Sensing Symposium
影响因子:
--
作者:
[N. Tack, B. A. Tama, A. Jebeli, V. Janeja, D. Engel, R. Williams]
通讯作者:
R. Williams
TSSA: two-step semi-supervised annotation for englacial radargrams on the Greenland ice sheet
TSSA:格陵兰冰盖冰川雷达图的两步半监督注释
DOI:
--
发表时间:
2023
期刊:
IEEE International Geoscience and Remote Sensing Symposium
影响因子:
--
作者:
[A. Jebeli, B. A. Tama, V. Janeja, N. Holschuh, C. Jensen, M. Morlighem, J. A MacGregor, M. Fahnestock]
通讯作者:
M. Fahnestock
DOI:
10.1109/bdcat56447.2022.00014
发表时间:
2022-12
期刊:
2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
影响因子:
--
作者:
[Xingyan Li;Jian Li;Zachary Williams;Xin Huang;M. Carroll;Jianwu Wang]
通讯作者:
Xingyan Li;Jian Li;Zachary Williams;Xin Huang;M. Carroll;Jianwu Wang
Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography
评估格陵兰冰下床地形的机器学习和统计模型
DOI:
10.1109/icmla58977.2023.00097
发表时间:
2023
期刊:
IEEE
影响因子:
--
作者:
[Yi, Katherine, Dewar, Angelina, Tabassum, Tartela, Lu, Jason, Chen, Ray, Alam, Homayra, Faruque, Omar, Li, Sikan, Morlighem, Mathieu, Wang, Jianwu]
通讯作者:
Wang, Jianwu
共 16 条
Collaborative Research: SCIPE: Enhancing the Transdisciplinary Research Ecosystem for Earth and Environmental Science with Dedicated Cyber Infrastructure Professionals
-
批准号:2321009
-
项目类别:Standard Grant
-
资助金额:$99.97万
-
财政年份:2023
-
负责人:Vandana Janeja
-
依托单位:
EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Improving Human Discernment of Audio Deepfakes via Multi-level Information Augmentation
-
批准号:2210011
-
项目类别:Standard Grant
-
资助金额:$29.98万
-
财政年份:2022
-
负责人:Vandana Janeja
-
依托单位:
海外基金