EMSW21-RTG: Statistics and Machine Learning for Scientific Inference

统计课程要求对统计理论进行过多的前期投资，而在“大科学”领域，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个培训项目将让学生及早接触跨学科研究，向他们展示统计学和机器学习思想的科学重要性，以及该学科的智力深度。研究生将接受关于跨学科互动、沟通技巧和教学的指导和指导反馈。博士后研究员将成为多产的研究人员，他们了解他们作为研究实验室的教员和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，使得目前的劳动力生产速度危险地不足。卡内基梅隆大学统计系将对本科生、研究生和博士后研究员进行综合培训，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联系的基础上。卡内基梅隆大学将从广泛的量化学科招收学生，重点是计算机科学。卡内基梅隆大学已经有一个异常庞大的本科生统计学项目。新的努力将加强对这些学生的培训，并吸引更多的高能力学生成为进入数学科学的管道的一部分。

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.