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 virtual seminar series, (2) an online bank of video tutorials, code, and slide decks made by and for atmospheric chemists on machine learning and data science topics, including our public archive of past speakers, and (3) an email list for questions and discussion.
The monthly seminar is core to SLAC and is designed and moderated to be useful (not just another obligation). Seminars consist of a recorded 40 minute Zoom talk on recent research at the intersection of machine learning and atmospheric chemistry/science, followed by an off-the-record question session. We invite speakers to go in-depth on methodology, such as by including code walkthroughs, in addition to high-level discussion.
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 and tutorial archive which we have catalogued since Spring 2021. The public can access recorded talks through our YouTube channel.