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; 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.

统计课程需要对统计理论进行过多的前期投资，这是许多``大型科学''领域中最初认为是不必要的``大科学''领域的。 卡内基·梅隆（Carnegie Mellon）的培训计划将使学生尽早暴露于跨学科研究，向他们展示了统计和机器学习中思想的科学重要性以及该主题的智力深度。研究生将获得有关跨学科互动，沟通技巧和教学的指导和指导反馈。博士后研究员将成为有效的研究人员，他们了解他们将作为研究实验室的教职员工或成员所面临的各种角色和责任。科学机构的统计需求非常巨大，并且迅速增长，使当前的劳动力生产率危险地不足。 卡内基·梅隆大学（Carnegie Mellon University）的统计系将在一项综合计划中培训本科生，研究生和博士后研究员，该计划强调在科学研究中应用统计和机器学习方法。该计划将基于与计算神经科学，计算生物学和天体物理学的现有联系。CarnegieMellon将从广泛的定量学科中招募学生，重点是计算机科学。 卡内基·梅隆（Carnegie Mellon）已经拥有一项异常大的本科统计计划。新的努力将加强对这些学生的培训，并吸引更多能力的学生成为进入数学科学的管道的一部分。