EMSW21-RTG: Statistics and Machine Learning for Scientific Inference

EMSW21-RTG：科学推理的统计和机器学习

• 批准号: 0943577
• 负责人: Robert Kass
• 金额: $ 35万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0943577&HistoricalAwards=false
• 关键词: EMSW21; RTG; Statistics; Machine; Learning

Statistics curricula have required excessive up-front investment in statistical theory, which many quantitatively-capable students in ``big science'' fields initially perceive to be unnecessary. A training program at Carnegie Mellon will expose students to cross-disciplinary research early, showing them the scientific importance of ideas from statistics and machine learning, and the intellectual depth of the subject. Graduate students will receive instruction and mentored feedback on cross-disciplinary interaction, communication skills, and teaching. Postdoctoral fellows will become productive researchers who understand the diverse roles and responsibilities they will face as faculty or members of a research laboratory.The statistical needs of the scientific establishment are huge, and growing rapidly, making the current rate of workforce production dangerously inadequate. The Department of Statistics at Carnegie Mellon University will train undergraduates, graduate students, and postdoctoral fellows in an integrated program that emphasizes the application of statistical and machine learning methods in scientific research. The program will build on existing connections with computational neuroscience, computational biology, and astrophysics.Carnegie Mellon will recruit students from a broad spectrum of quantitative disciplines, with emphasis on computer science. Carnegie Mellon already has an unusually large undergraduate statistics program. New efforts will strengthen the training of these students, and attract additional highly capable students to be part of the pipeline entering the mathematical sciences.

统计课程要求对统计理论进行过多的前期投资，而“大科学”领域中许多有定量能力的学生最初认为这是不必要的。 卡内基梅隆大学的一项培训计划将使学生尽早接触跨学科研究，向他们展示统计和机器学习思想的科学重要性，以及该主题的知识深度。研究生将接受指导和指导反馈跨学科的互动，沟通技巧和教学。博士后研究员将成为富有成效的研究人员，他们了解他们作为研究实验室的教师或成员将面临的各种角色和责任。科学机构的统计需求巨大，而且增长迅速，使得目前的劳动力生产率严重不足。 卡内基梅隆大学统计系将在一个综合项目中培养本科生、研究生和博士后研究员，该项目强调统计和机器学习方法在科学研究中的应用。该计划将建立在与计算神经科学，计算生物学和天体物理学现有的联系。卡内基梅隆大学将从广泛的定量学科招收学生，重点是计算机科学。 卡耐基梅隆大学已经有了一个异常庞大的本科统计学项目。新的努力将加强这些学生的培训，并吸引更多的高能力的学生成为进入数学科学管道的一部分。