Machine Learning in the Era of Large Astronomical Surveys

大型天文观测时代的机器学习

• 批准号: 1916991
• 负责人: Jason Prochaska
• 金额: $ 2万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916991&HistoricalAwards=false
• 关键词: Machine; Learning; Era; Large; Astronomical

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.

该奖项为2019年卡维理天体物理学暑期项目（KSPA）提供部分支持。 今年的主题是“大型天文巡天时代的机器学习”。 该项目将于2019年7月9日至8月16日在UCSC校园举办。KSPA是一项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，解决天体物理学中的热点问题。 经过一周的介绍性讲座和讨论后，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想法进行合作。 许多项目随后在会议论文集或参考期刊上发表。 2019年KSPA以天文学机器学习技术为重点，强调大型天文数据集的分析和可视化。KSPA积极并成功地推动多学科研究，培养了多元化的研究生群体，其中大多数人在过去继续了一流的学术生涯。 该项目强调多样性，其国际性促进了文化交流。 对计算机领域女性的重视将包括实现和维持性别平衡的最佳实践。 另一个重点是让行业专业人士参与他们的最新技术如何帮助天文学。 KSPA 可以满怀信心地期望继续培养新一代的跨学科科学家。 通过在学生职业生涯的早期向学生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至关重要。该奖项反映了 NSF 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。