This website is about a new NSF-funded cross-training program for graduate students and early career researchers in data science and atmospheric science to foster interdisciplinary “AI + HPC + Atmospheric Science” research and education using advanced cyberinfrastructure (CI) resources and techniques. The training consists of instruction in the areas of data, computing, and atmospheric sciences supported by teaching assistants, followed by faculty-guided project research in a multidisciplinary team of participants from each area. Participating graduate students, post-docs, and junior faculty from around the nation will be exposed to multidisciplinary research experiences and have the opportunity for significant career growth. In the end of the training, participants will learn how to conduct “Reproducible, Uncertainty-Aware and Scalable AI for Atmospheric Science”.
Acknowledgement
The current project is funded by the grant CyberTraining: Implementation: Small: Training of AI + Atmospheric Science with Focuses on Reproducibility, Uncertainty Awareness and Scalability, 2026/8-2029/7, grant no. OAC-2612212, from the National Science Foundation.
In 2018-2020, UMBC also received its first CyberTraining grant CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources from the National Science Foundation (grant no. OAC-1730250). More information of this past CyberTraining grant can be found at the CyberTraining 2018-20 web page of this website.