news
| May 22, 2026 | Sometimes it is nice to reflect upon the natural beauty of the atmospheric systems we research. We were lucky to have been selected as the first place finisher in the 2026 AOSS photo contest with our entry “Throizon”. Congratulations to all the other entries this year! |
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| Mar 17, 2026 | We are excited to share that KingLab has received support through the NVIDIA Academic Grant Program. This award will support our work on evaluating foundation models for precipitation and extreme weather applications, with a focus on interpretability, robustness, and scientific reliability. Want to collaborate? Reach out! |
| Nov 02, 2025 | We are excited to participate in the 106th AMS Annual Meeting’s Intermediate Machine Learning in Python for Environmental Science Problems, where we will be leading groups through interactive exercises on ML model selection and interpretability. Registration is now open! |
| Oct 17, 2025 |
 Are our models right for the right reasons? Check out our recently published paper on ML interpretability in the geosciences which examines this question: https://doi.org/10.1029/2025JH000769.
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| Sep 26, 2025 | Towards Data Science Article on Recent Precipitation Projects |
| Jul 05, 2025 |
We’ll be attending the 41st International Conference on Radar Meteorology this August to present our work on interpretable machine learning in the atmospheric sciences!   Looking forward to connecting, feel free to reach out if you’ll be there! |
| Jul 04, 2025 | Seeking Incoming Masters and PhD Students — Applied ML, Precipitation, Spaceborne Radar |
| Jul 04, 2025 |
The MLAP group website is live  ! Check back for updates soon.
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