EMSW21 - RTG: STATISTICS AND MACHINE LEARNING FOR SCIENTIFIC INFERENCE

统计课程需要对统计理论进行过多的前期投资，而许多“大科学”领域具有定量能力的学生最初认为这是不必要的。 卡内基梅隆大学的研究培训项目让学生尽早接触跨学科研究，向他们展示统计学和机器学习思想的科学重要性，以及该学科的知识深度。研究生接受有关跨学科互动、沟通技巧和教学的指导和指导反馈。博士后研究员成为富有成效的研究人员，他们了解自己作为研究实验室的教员或成员将面临的不同角色和责任。科学机构的统计需求巨大，而且增长迅速，使得当前的劳动力生产率严重不足。 卡内基梅隆大学统计系的研究培训项目在一个强调统计和机器学习方法在科学研究中应用的综合环境中培训本科生、研究生和博士后。该项目建立在与计算神经科学、计算生物学和天体物理学的现有联系之上。 卡内基梅隆大学正在招收来自广泛的定量学科的学生，重点是计算机科学。 卡内基梅隆大学已经拥有异常庞大的本科统计项目。新的努力将加强对这些学生的培训，并吸引更多有能力的学生加入数学科学领域。

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 research training program at Carnegie Mellon exposes 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 receive instruction and mentored feedback on cross-disciplinary interaction, communication skills, and teaching. Postdoctoral fellows 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 research training program in the Department of Statistics at Carnegie Mellon University trains undergraduates, graduate students, and postdoctoral fellows in an integrated environment that emphasizes the application of statistical and machine learning methods in scientific research. The program builds on existing connections with computational neuroscience, computational biology, and astrophysics. Carnegie Mellon is recruiting 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.