This page lists upcoming STAR Science Forum seminars.
Presentation materials for seminars will be
posted with each scheduled talk when available.
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STAR Seminars During the COVID-19 Pandemic
We will continue to schedule and present STAR seminars even though most contributors and attendees are
currently teleworking. However, all seminars scheduled to take place on or before 30 April 2020
will be presented via remote access only. This will be true even if the seminar was originally listed
with both remote access and a physical location. If you have questions about attending a specific seminar, please
reach out to Stacy.Bunin@noaa.gov.
All seminar times are given in Eastern Time
8 April 2020
||Special Seminar Series on AI: Understanding Key Components of the Atmospheric Science Machine Learning Pipeline
||David John Gagne, NCAR
|Date & Time:
||8 April 2020
11:30 am - 12:30 pm ET
||Via webinar only
STAR Science Seminars
Note: This series will be presented online only.Presenter(s):David John Gagne, NCARSponsor(s):
STAR Science Seminar Series: Special Seminar Series on AIRemote Access:
WebEx:Event Number: 909 492 412
Event address for attendees:https://noaa-nesdis-star.webex.com/noaa-nesdis-star/j.php?MTID=m8f8f05fb2abf3fd586202cc6644da0b7
+1-415-527-5035 US Toll
Access code: 909 492 412Abstract:The success of a machine learning system depends on not only the choice of machine learning algorithm but also on how the the whole machine learning pipeline is constructed. In this presentation, the key components of the machine learning pipeline, including problem definition, preprocessing, choosing appropriate algorithms, training, evaluation, and interpretation will be described. Common approaches in the atmospheric sciences for each component will be explained and linked with examples from machine learning applications in the atmospheric sciences. Finally, challenges of transitioning machine learning systems to operational use will be discussed. Bio(s):
David John Gagne is a Machine Learning Scientist in the Computational Information Systems Laboratory (CISL) and the Research Applications Laboratory (RAL) at the National Center for Atmospheric Research (NCAR). His research focuses on developing machine learning systems to improve the prediction and understanding of high impact weather, and to enhance weather and climate models. During his time at NCAR, he has collaborated with interdisciplinary teams to produce machine learning systems to study hail, tornadoes, hurricanes, and renewable energy. He has also developed short courses and hackathons to provide atmospheric scientists hands-on experience with machine learning. Gagne received his Ph.D. in meteorology from the University of Oklahoma in 2016 and completed an Advanced Study Program postdoctoral fellowship at NCAR in 2018. In addition to his duties at NCAR, he also serves as chair of the American Meteorological Society Artificial Intelligence Committee.
Stacy Bunin, firstname.lastname@example.org
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