CHE/DMS Innovation Lab: Learning the Power of Data in Chemistry

在化学系和数学科学系的支持下，乔治亚理工学院的霍晓明教授和密歇根大学的Paul Zimmerman教授将在为期五天的沉浸式创新实验室中汇集化学和数据科学方面的研究人员。创新实验室是由“沙坑”研讨会演变而来的，研究资助机构曾使用沙坑研讨会进行头脑风暴和团队组建，以加速科学创新。在为期五天的面对面互动中，参与者将通过案例研究演示和训练营活动学习彼此的语言，构建新的研究问题，围绕这些想法组建跨学科团队，并启动包括来自化学和数据科学社区的科学家在内的合作项目。这些项目将利用机器学习和数据驱动的统计技术来解决具有挑战性的化学问题，并分析复杂的化学数据集。创新实验室的主要目标是在化学家和数据科学家之间建立新的联系，并激发新的研究问题和新的跨学科合作。创新实验室将包括研究生以及处于不同职业阶段的参与者，以加强未来劳动力的发展，并促进妇女和其他代表性不足的群体在研究界的参与。当标准的研究模式被全新的思维方式所取代时，科学就会取得巨大的进步。这种进步的潜力已经在数据科学和化学的界面上得到了认可，化学科学可以从统计学、计算机科学和数学的新兴策略中极大地受益。该项目汇集了历史上不相关学科的专家，以加速化学数据科学这一新兴领域的发展。创新实验室的成果预计将是数据和计算机科学社区与化学的更深层次的接触，激发两个社区之间的新合作，开创新的思维方式，以及远远超出实验室本身时间框架的联合项目。这些项目有可能在化学领域产生深远的科学影响，激发新的方法和算法的发展，并成为数据驱动和化学方法之间相互作用的主要例子。基本的数学和统计原理将应用于解决重要的化学问题。相反，化学中具有挑战性的数据集和问题可能会激发数据科学中意想不到的方法发展。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。

With the support from the Division of Chemistry and the Division of Mathematical Sciences, Professor Xiaoming Huo of the Georgia Institute of Technology and Professor Paul Zimmerman of University of Michigan will bring together researchers in chemistry and data science for an immersive five-day Innovation Lab. Innovation Labs are an evolution of "sandpit" workshops that have been used by research funding agencies for brainstorming and team formation, to accelerate scientific innovation. Over five days of in-person interaction, participants will learn to speak each others' languages through case study presentations and bootcamp activities, frame new research questions, form interdisciplinary teams around these ideas, and initiate collaborative projects that include scientists from both the chemistry and data science communities. The projects will utilize machine learning and data-driven statistical techniques to tackle challenging chemical problems and the analysis of complex chemical data sets. A primary objective of the Innovation Lab is to forge new connections between chemists and data scientists, and stimulate new research problems and new interdisciplinary collaborations. The Innovation Lab will include graduate students as well as participants at different career stages, to strengthen future workforce development and promote the participation of women and other underrepresented groups in the research community. Dramatic advances in science can result when standard modes of research are replaced with completely new ways of thinking. The potential for such advances has been recognized at the interface of data science and chemistry, a key area where chemical sciences can greatly benefit from emerging strategies in statistics, computer science, and mathematics. This project brings together experts from historically unconnected disciplines to accelerate the development of the nascent field of chemical data science. The outcome of the Innovation Lab is anticipated to be a deeper engagement of the data and computer science communities with chemistry, sparking new collaborations between the two communities, the initiation of new ways of thinking, and joint projects that extend well beyond the time frame of the Lab itself. These projects have the potential to achieve profound scientific impact in chemistry, inspire new methodological and algorithmic development, and serve as prime examples of the interplay between data-driven and chemical approaches. Fundamental mathematical and statistical principles will be applied to tackle important chemistry problems. Conversely, the challenging data sets and problems from chemistry may inspire unanticipated methodological developments in data science.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.