Applications of artificial intelligence in Earth system science

Dr. Charlie Becker is a Machine Learning Scientist on the Machine Integration and Learning of Earth Systems (MILES) team within NCAR’s Computational Information Systems Laboratory (CISL) where he has worked for the past six years. His background includes an undergraduate degree in Meteorology from MSU Denver and a Master’s in Hydrology from Boise State University. His work within the MILES group centers on utilizing various machine learning methods to improve weather and climate models. He is also interested in interactive visualization to speed up the scientific process and the development of these models. In addition to his work at NCAR, he serves as an adjunct faculty member at MSU Denver teaching computer programming for meteorologists.

Dr. Nihanth Cherukuru is a Scientist and leads the Technology Exploration Group (TEG) at the NSF National Center for Atmospheric Research (NSF NCAR). TEG focuses on interdisciplinary applied research, helping scientists, policymakers, and the public understand the increasingly complex data produced by modern climate and weather models. He develops user-centered data visualization systems that combine AI/ML, immersive technologies, and scientific software to make large geoscience datasets more interpretable and actionable. Visualization systems developed through this work have been showcased at the White House Frontiers Conference, on Capitol Hill at the House Earth & Space Science Caucus, and at the USA Science and Engineering Festival.

Negin Sobhani, PhD is a high-performance computing (HPC) consultant at NSF NCAR’s Computational and Information Systems Laboratory (CISL). Her work sits at the intersection of HPC, AI/ML, and Earth system modeling, where she helps scientists to use advanced computing and AI/ML to accelerate discovery for weather, climate, and environmental applications. Her expertise includes training and scaling AI models and scientific workflows on GPU-powered supercomputers, developing scalable data pipelines, and optimizing large scientific codes to enable faster modeling of complex Earth system processes. 

Negin earned her PhD in Chemical Engineering from the University of Iowa, where her research focused on the performance optimization of regional weather and air quality models. At NSF NCAR, she is also actively involved in training and community engagement at NSF NCAR – organizing tutorials, workshops, and short courses on AI/ML and scientific computing while championing open science, reproducibility, and interdisciplinary collaboration.