Machine Learning in the Era of Large Astronomical Surveys

点击翻译按钮获取中文摘要

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.