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.
The statistical needs of the scientific establishment are huge, and growing rapidly, making the current rate of workforce production dangerously inadequate. Part of the problem is that many quantitatively-capable students do not perceive the value of in-depth statistical training. The Department of Statistics at Carnegie Mellon University initiated a training program aimed at helping to produce additional, high-quality contributors to the workforce in mathematical sciences. The emphasis of the program is on ``big science'' areas of cross-disciplinary research. Carnegie Mellon has an unusually large group of undergraduate statistics majors, and has a history of successful mentored, cross-disciplinary statistical training at the graduate and postgraduate levels. Carnegie Mellon trained undergraduates, graduate students, and postdoctoral fellows in an integrated program focusing on the application of statistical and machine learning methods in scientific research. Trainees were recruited from a broad spectrum of quantitative disciplines, tapping into a big pool of talented students, with emphasis on computer science. Trainees worked on projects in many domains, including computational neuroscience, computational biology, and astrophysics. Undergraduates were able to work on cutting-edge research projects. Graduate and postdoctoral trainees received mentored feedback on cross-disciplinary interaction, communication skills, and teaching, which helped them become productive researchers who understand the diverse roles and responsibilities they will face as faculty or members of a research laboratory.
