Joint Session 60 Incorporating Data Science and Machine Learning into Atmospheric Science Education

Thursday, 16 January 2020: 8:30 AM-9:30 AM
156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; the 29th Conference on Education; and the Events )
Cochairs:
David John Gagne II, Univ. of Oklahoma, Meteorology, Norman, OK and Dorit Hammerling, Jupiter Technology, Boulder, CO

Data science and machine learning are increasingly being incorporated into all 3 sectors of the atmospheric science enterprise, and graduating students with these skills are in high demand. How are atmospheric science programs at universities changing their curricula and classes to incorporate data science and machine learning skills? How are research labs, government agencies, and private companies training their staff to use data science and machine learning in their work? This session would feature presentations from representatives of the different atmospheric science sectors to discuss what education efforts they are undertaking in their institutions. What education methods, such as classes, short courses, seminar series, have worked well? What challenges remain in educating the broader community in these topics? This session would be sponsored jointly by the Conference on AI for Environmental Science and the Education Conference.

Papers:
8:30 AM
J60.2
Client-Driven, University Student Capstone Project in Environmental Machine Learning
Timothy J. Hall, The Aerospace Corporation, Greenbelt, MD; and E. B. Wendoloski
9:00 AM
J60.1A
Broadening of the AI Workforce through a Junior College Program
Philippe Tissot, Texas A&M Univ. - Corpus Christi, Corpus Christi, TX
9:15 AM
J60.3
Practical AI in the Classroom
Jianghao Wang, MathWorks, Natick, MA
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