SIPN2 Webinar - Machine Learning, 29 July 2020
Challenges and Opportunities for Applications in Sea-Ice Prediction
This webinar focused on the use of Machine Learning and Artificial Intelligence, on how such approaches can be applied in cryospheric research, as well as related challenges and limitations. Discussion included the use of advanced machine learning algorithms in climate science with attention on how to improve the prediction of future sea-ice. Participant questions and answers from speakers followed the presentations.
This one-hour webinar started at 8:00 am (Alaska Daylight Time)
The webinar has been archived and is available online.
Note: In addition to the Zoom presentation, a free livestream of this webinar was broadcast on YouTube — a facilitator provided support to those participants to submit written questions to the speakers.
This webinar was designed for the sea-ice research community and others interested in information about applications of machine learning in sea-ice forecasting. While this was an open event, attendees were advised that the discussions would largely be of a technical nature.
- Elizabeth A. Barnes, Associate Professor, Department of Atmospheric Science, Colorado State University
- Morteza Karimzadeh, Assistant Professor, Department of Geography, Colorado University Boulder
- Tom R. Andersson, Data Scientist at the British Antarctic Survey Artificial Intelligence Lab
Elizabeth (Libby) Barnes is an associate professor of Atmospheric Science at Colorado State University. She joined the CSU faculty in 2013 after obtaining dual B.S. degrees (Honors) in Physics and Mathematics from the University of Minnesota, obtaining her Ph.D. in Atmospheric Science from the University of Washington, and spending a year as a NOAA Climate & Global Change Fellow at the Lamont-Doherty Earth Observatory. Professor Barnes' research is largely focused on climate variability and change and the data analysis tools used to understand it. Topics of interest include earth system predictability, jet-stream dynamics, Arctic-midlatitude connections, subseasonal-to-seasonal (S2S) prediction (she is currently Task Force Lead for the NOAA MAPP Subseasonal-to-Seasonal (S2S) Prediction Task Force), and data science methods for earth system research (e.g. machine learning, causal discovery). She teaches graduate courses on fundamental atmospheric dynamics and data science and statistical analysis methods.
Morteza Karimzadeh is an assistant professor in the Department of Geography and affiliate assistant professor in the Department of Information Science at the University of Colorado (CU) Boulder. He joined CU from Purdue University, where he was a postdoctoral scientist at the School of Electrical and Computer Engineering. Morteza is a geospatial data scientist, with research cutting across geographic information retrieval, machine learning, geovisualization, and visual analytics. His primary research focuses on method development, spanning various domains including social media analytics, environmental data fusion and analysis, energy (resilience and production), situational awareness, precision agriculture, and digital humanities. His approach to research and development is human-centered, from visual design to ground truth creation, algorithm integration and evaluation, to domain deployment and field studies.
Tom Andersson is a Data Scientist at the British Antarctic Survey (BAS) Artificial Intelligence Lab and holds a Master's degree in Information Engineering from the University of Cambridge. Tom's research at BAS involves the application of cutting-edge machine learning algorithms to remote sensing and climate model data. Tom's work is in collaboration with the Alan Turing Institute in London. For more information, see: Tom Andersson’s website.