CAREER: Scalable Black-box Optimization for Scientific Discovery

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。寻找一个新的科学发现经常可以被视为一个优化问题。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质量很难提前估计，需要实验来测量，这使得这些优化问题成为“黑箱”，特别具有挑战性。该项目将建立新的技术，使从业者能够利用大量的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。这项研究与教育和推广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和（B）对数据驱动的科学发现感兴趣的学生，从中学水平的推广到新的本科课程的开发，旨在达到尽可能广泛的科学和工程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）的高维和结构化优化问题，以及（2）开发新的大规模概率机器学习方法，使这些优化问题中固有的开发与探索权衡得到仔细考虑。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。该项目将开发专门针对优化领域定制的新型深度表示模型，利用大量无监督和多任务数据来优化可以表示为图形，字符串，点云或图像的广泛类别的对象。该项目将扎根于具体的，具体的应用和合作，在治疗设计，认知科学的学习和阿尔茨海默氏症，以及能源技术的发展。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.