Machine Learning in the Era of Large Astronomical Surveys

该奖项为2019年Kavli天体物理学夏季项目（KSPA）提供部分支持。今年的主题是“大型天文调查时代的机器学习”。该项目将于2019年7月9日至8月16日在UCSC校园举行。KSPA是一个独特的研究生培训项目，将世界一流的科学家与约15名研究生聚集在一起，解决天体物理学领域的热门突出问题。在一周的介绍性讲座和讨论之后，时间主要用于研究。在指导学生在他们选择的创新研究项目上取得重大进展的同时，教师和博士后也有很多机会就新的想法进行合作。许多项目后来在会议论文集或评审期刊上发表。2019年KSPA重点关注天文学中的机器学习技术，强调大型天文数据集的分析和可视化。KSPA积极并成功地促进多学科研究，培养了多样化的研究生群体，其中大多数人在过去继续从事一流的学术事业。该项目强调多样性，其国际性促进了文化交流。强调计算机领域的女性将包括实现和维持性别平衡的最佳实践。另一个重点是让业内专业人士参与他们的最新技术如何帮助天文学。KSPA可以满怀信心地期待继续培养新一代的跨学科科学家。通过在学生的职业生涯早期向他们介绍数据科学和机器学习，本次研讨会将展示与统计学、计算机科学和行业同事的合作对于充分利用这些新兴技术的重要性。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。

This award provides partial support for the 2019 Kavli Summer Program in Astrophysics (KSPA). The topic this year is "Machine Learning in the Era of Large Astronomical Surveys". The program will be hosted on the UCSC campus from July 9 to August 16, 2019The KSPA is a unique graduate training program that brings world-class scientists together with about 15 graduate students to solve topical outstanding problems in astrophysics. After an introductory week of lectures and discussion, the time is primarily dedicated to research. While coaching students to make significant progress on their chosen innovative research projects, faculty and postdocs also have many opportunities to collaborate on new ideas. Many projects are later published, in either a conference proceeding or a refereed journal. With a focus on machine-learning techniques in astronomy, the 2019 KSPA emphasizes the analysis and visualization of large astronomical datasets.The KSPA actively and successfully promotes multidisciplinary research, training a diverse group of graduate students, the majority of whom have in the past continued to first-class academic careers. The program emphasizes diversity, and its international nature promotes cultural exchange. An emphasis on women in computing will include best practices towards achieving and maintaining gender balance. Another emphasis is to engage industry professionals in how their latest techniques can help astronomy. The KSPA can confidently expect to continue to train a new generation of cross-disciplinary scientists. By introducing students to data science and machine learning early in their careers, this workshop will demonstrate how collaboration with statistics, computer science, and industry colleagues is critical for fully leveraging these emerging technologies.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.