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

该项目包括规划和组织几个专业研讨会，将汇集多个领域的顶级专家，以塑造新的和新兴的多学科领域，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增长。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运行的自主机器人，涉及具有多个竞争目标的大型组织的供应链优化，认知神经科学将大脑电脉冲与解决问题等高级功能联系起来。为此，有必要促进跨学科合作，促进聚合研究，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。在利哈伊大学新的数据，智能系统和计算研究所（I-DISC）的保护下，该研究所建立在利哈伊在机器学习，优化和数据驱动决策等领域的研究专业知识的基础上，将组织四个研讨会，将汇集来自不同研究社区的领先研究人员，否则可能不会相互作用。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吸引力。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，可解决能源，健康，环境和水的巨大挑战。(2)机器人技术，其中机器学习的应用，也被称为机器人学习，近年来一直在快速增长，其中主要的重点是开发算法，以帮助机器人获得新的技能或通过传感来适应他们的环境。(3)供应链管理，特别关注将机器学习模型应用于规范性分析，例如优化，而不是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，例如预测客户需求。(4)认知神经科学，重点是理解大脑-认知-行为界面，这需要神经科学和计算建模方面的专业知识，机器学习和大数据科学，以便（a）实现对大脑数据中复杂模式的复杂分析，以及（B）深入了解假设的大脑-事实上，一个水平的实施可以产生可观察到的行为结果。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值进行评估来支持的和更广泛的影响审查标准。

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.