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CAREER: Scalable Black-box Optimization for Scientific Discovery

职业：科学发现的可扩展黑盒优化

基本信息

批准号：
2145644
负责人：
Jacob Gardner
金额：
$ 55.35万
依托单位：
University of Pennsylvania
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2027-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145644&HistoricalAwards=false
关键词：
CAREER Scalable Black box Optimization

项目摘要

Scientists across the natural sciences and engineering increasingly rely on data-driven approaches to assist them in making their discoveries. Searching for a new scientific discovery can frequently be cast as an optimization problem. For example, a biochemist searching for new therapeutics might seek to optimize the antiviral activity of a new molecule, or an engineer might optimize the aerodynamic efficiency of a new vehicle. Qualities like antiviral activity are difficult to estimate in advance and require experimentation to measure, making these optimization problems “black-box” and particularly challenging. This project will build novel technologies that enable practitioners to leverage large quantities of data to aid in solving these challenging problems, even for highly complex and structured objects like molecules or vehicles. This will empower scientists to more rapidly design the next generation of therapeutics, energy technologies and more. This research is coupled with education and outreach to a broad set of stakeholders, including (a) professionals in the natural sciences and engineering through direct collaboration, public tutorials, and open source software and (b) students interested in data-driven scientific discovery, from outreach at the middle school level to the development of new undergraduate curricula designed to reach the broadest possible science and engineering audiences.This project will focus broadly on two key research challenges: (1) developing methods for black-box optimization that scale to high dimensional and structured optimization problems over challenging domains like molecules, and (2) developing novel large scale probabilistic machine learning methods that enable careful consideration of the exploitation versus exploration trade-offs inherent in these optimization problems. A core theme through both of these challenges will be reducing complex discrete search spaces into well-organized continuous latent spaces extracted by deep neural networks. This project will develop novel deep representation models tailored specifically to the optimization domain, leveraging large quantities of unsupervised and multi-task data to enable optimization over broad classes of objects that can be represented as graphs, strings, point clouds or images. The project will be grounded in concrete, specific applications and collaborations in therapeutic design, cognitive science for learning and Alzheimer's disease, and the development of energy technologyThis 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.

自然科学和工程领域的科学家越来越依赖数据驱动的方法来帮助他们进行发现。寻找新的科学发现常常被看作是一个优化问题。例如，寻找新疗法的生物化学家可能会寻求优化新分子的抗病毒活性，或者工程师可能会优化新车辆的空气动力学效率。像抗病毒活性这样的特性很难提前估计，需要通过实验来测量，这使得这些优化问题成为“黑箱”，尤其具有挑战性。该项目将构建新颖的技术，使从业者能够利用大量数据来帮助解决这些具有挑战性的问题，即使是高度复杂和结构化的对象，如分子或车辆。这将使科学家能够更快地设计下一代疗法、能源技术等。这项研究与教育和广泛的利益相关者相结合，包括(a)通过直接合作，公共教程和开源软件的自然科学和工程专业人员，以及(b)对数据驱动的科学发现感兴趣的学生，从中学水平的外展到开发新的本科课程，旨在达到尽可能广泛的科学和工程受众。该项目将广泛关注两个关键的研究挑战：(1)开发黑盒优化方法，该方法可扩展到具有挑战性的领域（如分子）上的高维和结构化优化问题；(2)开发新的大规模概率机器学习方法，能够仔细考虑这些优化问题中固有的开发与勘探权衡。通过这两个挑战的核心主题将是将复杂的离散搜索空间减少到由深度神经网络提取的组织良好的连续潜在空间。该项目将开发专门为优化领域量身定制的新颖深度表示模型，利用大量无监督和多任务数据，对可以表示为图形、字符串、点云或图像的广泛类别的对象进行优化。该项目将以治疗设计、学习认知科学和阿尔茨海默病以及能源技术发展方面的具体、具体应用和合作为基础。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智力价值和更广泛的影响审查标准进行评估，被认为值得支持。