The Statistical Learning in Atmospheric Chemistry (SLAC) community is an online space supporting scientists interested in learning about and applying machine learning and data science methods in their research. Because these methods are relatively new to the discipline, SLAC is designed to supplement the support scientists get from their own research groups and collaborators — many find themselves the first in their circles to use a particular algorithm. SLAC provides members three things: (1) a monthly one-hour Zoom meeting for discussion, (2) an online bank of video tutorials, code, and slide decks made by and for atmospheric chemists on machine learning and data science topics, and (3) an email list for questions and discussion.
The monthly meeting is core to SLAC and is designed and moderated to be useful — not just another obligation. Meetings consist of an extended presentation by a community member or guest on methods, and a roundtable discussion following the talk. We ask speakers to go in-depth on methodology, such as by including code walkthroughs. SLAC isn’t primarily a forum for presenting research — we want this space to be focused on practical education.
Anyone at any career stage doing work related to atmospheric chemistry is welcome to join. Email one of the coordinators to be added to the mailing list. All members have full access to the past speakers archive which we have catalogued since Spring 2021.