Aaditya Ramdas – Checklists for Stat-ML assistant professors

(Also check out the checklists for Stat-ML PhD students)

Faculty in Statstics and Machine Learning (and related areas) are expected to perform a variety of duties that they have may never previously done: lead a research group, mentor students and postdocs with various backgrounds, apply for research grants, serve on thesis committees, show leadership in the profession (area chair or associate editor, organize workshops), teach a variety of courses, and so on. These are rather specialized skills which people have typically not received training on prior to becoming assistant professors, but are expected to be excellent at anyway.

We often expect young faculty to develop these expertise without the “tripod” of directed mentorship, deliberate practice and constructive feedback. Currently, I think young faculty partially succeed at this by some mixture of chance and personal motivation, and depending heavily on their department's motivation to provide mentorship. Further, many young faculty do not have structured ways to develop these skills.

The aim of the following checklists is primarily:

However, there are potential side-benefits:

Caveat 1: Ultimately, these are my opinions. If your opinions have a reasonably high correlation with mine, please feel free to share this page with others. If your opinions differ significantly from mine, perhaps make your own such page, so that students can make their own choices after reading multiple opinions.

Caveat 2: Such checklists are not meant to be complete in any sense (it would take too much time and effort). I might use them to begin a discussion with junior faculty in a group meeting (different topic each month, as needed), when I can answer a variety of related questions on each topic and add nuance to what I have written down. These are thus just beginner guides, or starting points, and entire booklets could be written on each.

To be written: