AI Institute for Learning-Enabled Optimization at Scale (TILOS)
AI 大规模学习优化研究所 (TILOS)
基本信息
- 批准号:2112665
- 负责人:
- 金额:$ 2000万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Cooperative Agreement
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-11-01 至 2026-10-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Improved optimizations of energy-efficiency, safety, robustness, and other criteria in engineered systems offer the promise of incalculable societal benefits. However, challenges of scale and complexity keep many real-world optimization needs beyond our reach. The mission of The National Artificial Intelligence (AI) Institute for Learning-enabled Optimization at Scale (TILOS) is to make impossible optimizations possible, at scale and in practice. The institute (a partnership of University of California, San Diego, Massachusetts Institute of Technology, National University, University of Pennsylvania, University of Texas at Austin and Yale University) will pioneer learning-enabled optimizations that transform chip design, robotics, communication networks, and other use domains that are vital to our nation’s health, prosperity and welfare. In TILOS, research, education, outreach and translation are holistically driven by what makes the nexus of AI/ML and optimization uniquely challenging at the leading edge of practice. Industry partners will interact closely with TILOS on both foundational research and its use-domain application. TILOS will build an openly accessible program of continuing education with long-term, lifelong learning and skills renewal as its central tenet. This institute will also broaden participation, building on the visible successes at its partner institutions that have reached underserved demographics from K-12 onward. Through these efforts, TILOS will discover, educate, and translate into real-world practice a new nexus of AI, optimization, and use. TILOS is organized around multiple virtuous cycles that unify AI and optimization, use domains, and the translation of AI-optimization breakthroughs into practice. A first virtuous cycle of AI and optimization, where each enables and amplifies the other, is at the heart of TILOS. Foundational research will pursue five main pillars: (i) bridging discrete and continuous optimization; (ii) distributed, parallel, and federated optimization; (iii) optimization on manifolds; (iv) dynamic decisions under uncertainty; and (v) nonconvex optimization in deep learning. A second virtuous cycle of challenges, inspirations and data-enabled validations connects the foundational research in AI-optimization with use-domain expertise. The initial use-domain foci bring diverse optimization challenges but inspire shared solutions with commonalities such as physical embeddedness, hierarchical-system context, underlying graphical models, safety and robustness as first-class concerns, and the bridging of human-guided and autonomous systems. A third virtuous cycle is one of translation and ever-tighter connections to the leading edge of practice. TILOS will leverage industry partnerships to accelerate impact via open standards, data sets and “data virtual reality”, and open source that democratize access to research enablement. Roadmaps of optimization formulations and progress metrics will draw researchers together and toward shared research goals. A fourth virtuous cycle with industry and the institutional partners spans both workforce development and the broadening of participation. Workforce development will identify and teach the skills and mindsets needed at the nexus of learning, optimization and practice, so as to provide skills renewal for the existing workforce as well as onramps for underserved demographics such as veterans or those seeing a career change. Broadening of participation will be pursued via the institute’s partnerships with community organizations and middle and high school educators, via tiers of engagement that span exposure, experience and environment.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.
改善工程系统中能效,安全性,鲁棒性和其他标准的优化提供了不可估量的社会福利的希望。但是,规模和复杂性的挑战使许多现实世界优化的需求无法实现。国家人工智能(AI)的大规模学习优化(TILOS)的使命是使不可能的优化在大规模和实践中成为可能。该研究所(加州大学圣地亚哥分校,马萨诸塞州大学,国立大学,宾夕法尼亚大学,奥斯汀大学和耶鲁大学的合作伙伴关系)将开发学习基于学习的优化,从而改变Chip Design,机器人技术,通信网络和其他对我们国家的健康,繁荣,繁荣,繁荣,繁荣,繁荣,繁荣,富裕。在TILOS中,研究,教育,宣传和翻译是由使AI/ML的联系和实践领先优势挑战的全面驱动的。行业合作伙伴将在基础研究及其使用域应用方面与TILOS紧密互动。 TILOS将建立一个公开访问的继续教育计划,以长期,终身学习和技能续签作为其中心宗旨。该研究所还将扩大参与度,这是基于其合作伙伴机构的可见成功,这些机构从K-12开始就达到了服务不足的人口统计。通过这些努力,Tilos将发现,教育并转化为现实世界实践,一种新的AI,优化和使用的联系。 tilos围绕多个虚拟周期组织,这些虚拟循环统一AI和优化,使用域以及对AI优化突破的翻译实践。 AI和优化的第一个虚拟循环,每个启用和放大器对方都是Tilos的核心。基础研究将追求五个主要支柱:(i)桥接离散和连续优化; (ii)分布,并行和联合优化; (iii)对流形的优化; (iv)不确定性下的动态决策; (v)深度学习中的非凸优化。挑战,灵感和支持数据的验证的第二个虚拟周期将AI优化的基础研究与使用域专业知识联系起来。最初的使用域焦点带来了潜水员优化的挑战,但启发了共同的解决方案,具有诸如物理嵌入性,分层系统环境,基本的图形模型,安全性和鲁棒性作为一流的关注以及人类引导和自治系统的弥合。第三个良性周期是与实践的前沿的翻译和越来越稳定的连接之一。 TILOS将利用行业合作伙伴关系通过开放标准,数据集和“数据虚拟现实”加速影响,并开源,以民主化对研究支持的访问。优化公式和进度指标的路线图将把研究人员融合在一起,并朝着共同的研究目标迈进。与行业和机构合作伙伴的第四个虚拟周期既跨越了劳动力发展和参与的扩大。劳动力发展将确定并教授学习,优化和实践联系的技能和思维方式,以便为现有劳动力以及对退伍军人或看到职业变革的人的服务不足的人口统计信息提供技能更新。将通过该研究所与社区组织以及中学和高中教育者的合作伙伴关系,通过跨越曝光,经验和环境的参与度来扩大参与。该奖项反映了NSF的法定任务,并且我们是否通过使用基金会的知识分子优点和更广泛的影响审查标准来通过评估来诚实地支持我们的支持。
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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Yusu Wang其他文献
Measuring Distance between Reeb Graphs
测量 Reeb 图之间的距离
- DOI:
10.1145/2582112.2582169 - 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
Ulrich Bauer;Xiaoyin Ge;Yusu Wang - 通讯作者:
Yusu Wang
Annotating Simplices with a Homology Basis and Its Applications
- DOI:
10.1007/978-3-642-31155-0_17 - 发表时间:
2012-01-01 - 期刊:
- 影响因子:0
- 作者:
Busaryev, Oleksiy;Cabello, Sergio;Yusu Wang - 通讯作者:
Yusu Wang
Local Versus Global Distances for Zigzag and Multi-Parameter Persistence Modules
Zigzag 和多参数持久性模块的本地距离与全局距离
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Ellen Gasparovic;Maria Gommel;Emilie Purvine;R. Sazdanovic;Bei Wang;Yusu Wang;Lori Ziegelmeier - 通讯作者:
Lori Ziegelmeier
Reeb Graphs: Approximation and Persistence
Reeb 图:近似和持久性
- DOI:
10.1145/1998196.1998230 - 发表时间:
2011 - 期刊:
- 影响因子:0.8
- 作者:
T. Dey;Yusu Wang - 通讯作者:
Yusu Wang
Yusu Wang的其他文献
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{{ truncateString('Yusu Wang', 18)}}的其他基金
Collaborative Research: AF: Small: Graph Analysis: Integrating Metric and Topological Perspectives
合作研究:AF:小:图分析:整合度量和拓扑视角
- 批准号:
2310411 - 财政年份:2023
- 资助金额:
$ 2000万 - 项目类别:
Standard Grant
AitF: Collaborative Research: Topological Algorithms for 3D/4D Cardiac Images: Understanding Complex and Dynamic Structures
AitF:协作研究:3D/4D 心脏图像的拓扑算法:理解复杂和动态结构
- 批准号:
2051197 - 财政年份:2020
- 资助金额:
$ 2000万 - 项目类别:
Standard Grant
Collaborative Research: I-AIM: Interpretable Augmented Intelligence for Multiscale Material Discovery
合作研究:I-AIM:用于多尺度材料发现的可解释增强智能
- 批准号:
2039794 - 财政年份:2020
- 资助金额:
$ 2000万 - 项目类别:
Standard Grant
Collaborative Research: I-AIM: Interpretable Augmented Intelligence for Multiscale Material Discovery
合作研究:I-AIM:用于多尺度材料发现的可解释增强智能
- 批准号:
1940125 - 财政年份:2019
- 资助金额:
$ 2000万 - 项目类别:
Standard Grant
AitF: Collaborative Research: Topological Algorithms for 3D/4D Cardiac Images: Understanding Complex and Dynamic Structures
AitF:协作研究:3D/4D 心脏图像的拓扑算法:理解复杂和动态结构
- 批准号:
1733798 - 财政年份:2017
- 资助金额:
$ 2000万 - 项目类别:
Standard Grant
AF: Small: Collaborative Research:Geometric and topological algorithms for analyzing road network data
AF:小型:协作研究:用于分析道路网络数据的几何和拓扑算法
- 批准号:
1618247 - 财政年份:2016
- 资助金额:
$ 2000万 - 项目类别:
Standard Grant
AF: Small: Analyzing Complex Data with a Topological Lens
AF:小:用拓扑透镜分析复杂数据
- 批准号:
1526513 - 财政年份:2015
- 资助金额:
$ 2000万 - 项目类别:
Standard Grant
AF: Small: Approximation Algorithms and Topological Graph Theory
AF:小:近似算法和拓扑图论
- 批准号:
1423230 - 财政年份:2014
- 资助金额:
$ 2000万 - 项目类别:
Standard Grant
AF: Small: Geometric Data Processing and Analysis via Light-weight Structures
AF:小型:通过轻量结构进行几何数据处理和分析
- 批准号:
1319406 - 财政年份:2013
- 资助金额:
$ 2000万 - 项目类别:
Standard Grant
AF: EAGER: Collaborative Research: Integration of Computational Geometry and Statistical Learning for Modern Data Analysis
AF:EAGER:协作研究:现代数据分析的计算几何与统计学习的集成
- 批准号:
1048983 - 财政年份:2010
- 资助金额:
$ 2000万 - 项目类别:
Standard Grant
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- 批准号:79060001
- 批准年份:1990
- 资助金额:2.5 万元
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- 批准年份:1990
- 资助金额:3.0 万元
- 项目类别:地区科学基金项目
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