TRIPODS+X:VIS: The DISC Institute Workshop Series on Machine Learning + X.

TRIPODS X:VIS：DISC 研究所机器学习 X 研讨会系列。

• 批准号: 1839353
• 负责人: Lawrence Snyder
• 金额: $ 19.94万
• 依托单位: Lehigh University
• 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: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839353&HistoricalAwards=false
• 关键词: TRIPODS+X; VIS; DISC; Institute; Workshop

This project encompasses the planning and organization of several specialized workshops that will bring together top experts in multiple areas to shape new and emerging multidisciplinary fields, tapping the tremendous recent surge in the adoption of machine learning tools in various areas of science and engineering. The premise of this project is the need for sophisticated computational tools to analyze data and improve our ability to understand and harness phenomena associated with complex domains such as chemical processes, autonomous robots operating in open and dynamic environments, supply chain optimization involving large organizations with multiple and competing objectives, and cognitive neuroscience bridging electrical brain impulses and high-level functions such as problem solving. Towards this end it is necessary to foster interdisciplinary collaborations and to promote convergent research and develop fertile space for collaborations among industrial, academic, and governmental partners to attack some of the most pressing problems in technology and society. Under the umbrella of the new Institute for Data, Intelligent Systems, and Computation (I-DISC) at Lehigh University, which builds upon the foundation of Lehigh research expertise in areas such as machine learning, optimization, and data-driven decision making, four workshops will be organized that will bring together leading researchers from different research communities that otherwise may not interact. All of these workshops are on newly emerging topics which are expected to gain significant traction in the near future. These topics are as follows: (1) Chemistry, chemical engineering, materials science, and related disciplines where machine learning is used to elucidate and design complex processes (chemical/biological, engineered/natural) or material systems with wide ranging applications addressing grand challenges in energy, health, environment, and water. (2) Robotics, where applications of machine learning, also known as robot learning, has been rapidly growing in recent years, where the main focus has been to develop algorithms to assist robots to acquire novel skill or adapt to their environment through sensing. (3) Supply chain management with the specific focus on applying machine learning models for prescriptive analytics, such as optimization, in contrast to already popular use of machine learning (deep learning) models for predictive and descriptive analytics, such as predicting customer demands. (4) Cognitive Neuroscience with the focus on understanding the brain-cognition-behavior interface, which requires expertise in neuroscience as well as computational modeling, machine learning and big data science in order (a) to enable sophisticated analyses of complex patterns in brain data and (b) to provide insight into how hypothesized brain-level implementations could in fact produce observed behavioral outcomes.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.

该项目包括计划和组织几个专业研讨会，这些研讨会将汇集多个领域的顶尖专家，以塑造新兴的多学科领域，利用最近在科学和工程各个领域采用机器学习工具的巨大激增。该项目的前提是需要复杂的计算工具来分析数据，并提高我们理解和利用与复杂领域相关的现象的能力，例如化学过程，在开放和动态环境中操作的自主机器人，涉及具有多个和竞争目标的大型组织的供应链优化，以及连接脑电脉冲和高级功能（如解决问题）的认知神经科学。为了实现这一目标，有必要促进跨学科合作，促进融合研究，并为工业、学术和政府合作伙伴之间的合作开发肥沃的空间，以解决技术和社会中一些最紧迫的问题。在里哈伊大学新成立的数据、智能系统和计算研究所（I-DISC）的保护下，将组织四个研讨会，汇集来自不同研究社区的领先研究人员，否则这些研究社区可能不会互动。该研究所建立在里哈伊大学在机器学习、优化和数据驱动决策等领域的研究专长的基础上。所有这些研讨会都是关于新出现的主题，预计在不久的将来会获得重大的牵引力。这些主题如下：(1)化学，化学工程，材料科学和相关学科，其中机器学习用于阐明和设计复杂的过程（化学/生物，工程/自然）或材料系统，具有广泛的应用，解决能源，健康，环境和水方面的重大挑战。(2)机器人技术，其中机器学习的应用，也称为机器人学习，近年来发展迅速，其主要重点是开发算法，以帮助机器人获得新的技能或通过感知适应环境。(3)供应链管理，侧重于将机器学习模型应用于规定性分析，如优化，与已经流行的机器学习（深度学习）模型用于预测和描述性分析（如预测客户需求）形成对比。(4)认知神经科学，重点是理解大脑-认知-行为界面，这需要神经科学以及计算建模、机器学习和大数据科学方面的专业知识，以便(a)能够对大脑数据中的复杂模式进行复杂分析，(b)深入了解假设的大脑层面实现如何实际上产生观察到的行为结果。该奖项反映了美国国家科学基金会的法定使命，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。