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Abstract:
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Carnegie Mellon Statistics is currently in the last stages of a five year NSF Research Training Grant in the Mathematical Sciences ("Statistics and Machine Learning for Scientific Inference"). Our departmental goals were: 1) provide training to postdoctoral scholars through classroom experience and collaboration with faculty on interdisciplinary research projects, 2) decrease time-to-degree and increase research output for graduate students through early engagement and reduced department obligations, and 3) develop new courses and educational materials that reflect the intersection of statistics and machine learning in today's modern problems. To do all three simultaneously has been, to say the least, tricky. In this talk, we give an overview of the approaches we tried including their (sometimes unexpected) consequences. Highlights include increasing the size of our PhD graduate class, launching a joint undergraduate major in statistics and machine learning, revamping statistical computing at all levels, and expanding our undergraduate summer research program. We will also discuss our efforts to improve diversity and share wisdom gained and lessons learned.
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