Core members of the CIRA ML team
- Imme Ebert-Uphoff (CIRA – ML group lead, faculty – ECE): ML for satellite applications, image-to-image translation, explainable AI (XAI), ethics & AI.
- Jason Apke (CIRA): Optical flow for environmental science applications.
- Akansha Singh Bansal (CIRA postdoc): Self-supervised learning, Deep learning, ML for satellite applications
- Galina Chirakova (CIRA): ML for tropical cyclones.
- Mark DeMaria (CIRA): ML for tropical cyclones.
- Eric Goldenstern (ATS Ph.D. student): ML for precipitation retrieval from satellites
- John M. Haynes (CIRA): ML for satellite applications
- Katherine Haynes (CIRA): ML for satellite applications, ML for tropical cyclones
- Kyle Hilburn (CIRA): ML for satellite applications
- Ryan Lagerquist (CIRA,NOAA-GSL): ML for satellite applications, ML for radiative transfer, explainable AI (XAI)
- Yoonjin Lee (CIRA postdoc): ML for satellite applications
- Yingzhao Ma (CIRA): Bayesian ML for radar applications
- Marie McGraw (CIRA postdoc): ML for tropical cyclones
- Steve Miller (CIRA director, faculty – Atmospheric Science): ML for Day-Night Band.
- Kate Musgrave (CIRA): ML tropical cyclones
- Yoo-Jeong Noh (CIRA): ML applications for satellite cloud detection and retrievals
- Paul Roebber (CIRA): Distinguished Professor – Atmospheric Science, University of Wisconsin, Milwaukee.
- Matt Rogers (CIRA): CIRA/ML outreach
- Andrea Schumacher (CIRA): tropical cyclones, risk communication for ML
- Charles White (CIRA postdoc): ML for satellite applications.
Close collaborators at NOAA
We work with the following NOAA scientists on almost a daily basis:
- Chris Slocum (NOAA-NESDIS/STAR/RAMMB, located at CIRA): ML for tropical cyclones
- John Knaff (NOAA-NESDIS, located at CIRA): tropical cyclones
- Jebb Q. Stewart (NOAA-GSL): ML for satellite applications, ML for radiative transfer
- Christina Kumler (NOAA-GSL / CIRES): ML for satellite applications
- Dave Turner (NOAA-GSL): radiative transfer, clouds and model verification
Collaborators through the AI Institute
We also work closely with the following scientists through the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). See also the AI Institute page for more information:
- Chuck Anderson (faculty – Computer science, CSU): machine learning
- Elizabeth Barnes (faculty – Atmospheric science, CSU): climate science, incl. subseasonal-to-seasonal prediction, machine learning, incl. XAI and robust AI
- Antonios Mamalakis (postdoc, Atmospheric science, CSU): machine learning for environmental sciences, explainable AI (XAI), benchmark development
- Jason Stock (Ph.D. student, Computer science, CSU): simplifying machine learning, interpretable-by-design, explainable AI
- Lander Ver Hoef (Ph.D. student, Mathematics, CSU): topological data analysis and applications to satellite data
- Amy McGovern (Univ. of Oklahoma) – ML for environmental science, XAI, AI & ethics
- David John Gagne (NCAR) – ML for environmental science, XAI, AI & ethics
- Julie Demuth (NCAR) – weather risk communication
- Ann Bostrom (Univ. of Washington) – risk communication, environmental policy
- Plus many other members of the AI2ES team.
NOAA Center for AI (NCAI)
Through the AI Institute we also work closely with the NOAA Center for Artificial Intelligence (NCAI), e.g., to organize the NCAR Summer School on Trustworthy AI for Environmental Science (TAI4ES) (6/27-07/01/2022) and other training activities.