Joint Session 55 Incorporating Data Science and Machine Learning into Atmospheric Science Education

Thursday, 16 January 2020: 8:30 AM-9:30 AM
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs:
David John Gagne II, NCAR, Boulder, CO and Dorit Hammerling, Colorado School of Mines, Golden, 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
J60.1
Atmospheric Sciences + Data Science at the University of Illinois Urbana-Champaign
Anna E. Nesbitt, University of Illinois Urbana-Champaign, Urbana, IL; and S. W. Nesbitt, B. F. Jewett, R. L. Sriver, S. Lasher-Trapp, R. J. Trapp, and R. M. Rauber

8:45 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
J60.3
Practical AI in the classroom
Jianghao Wang, MathWorks, Natick, MA

9:15 AM
J60.4
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner