Statistical Learning in
Welcome to SLAC
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 are in two parts: (1) a 30-minute roundtable focused on practical questions about methods, and (2) an extended presentation by a community member or guest on methods. The first section often involves brainstorming to solve data science problems encountered by a community member over the last month. For the second section, 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.