I'm an HPC consultant and computational scientist with 10+ years of experience at the intersection of AI/ML, high-performance computing, and Earth system science 🌎. I specialize in developing, scaling, and optimizing distributed training and inference workflows on multi-node, multi-GPU HPC architectures — with a focus on advancing weather and climate forecasting using deep learning and GPU-accelerated computing.
I currently work at the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NSF-NCAR), where I help researchers scale and optimize scientific AI workloads and build GPU-native data pipelines for weather and climate applications. I have an academic background in numerical weather prediction and air quality modeling, and my Ph.D. thesis focused on performance analysis and optimization of weather and air quality models.
My current work spans scaling AI/ML workflows on supercomputers for Earth system science, building community-driven infrastructure, and championing open science practices across the geosciences.
🖥️ HPC Consultant & Computational Scientist at NSF-NCAR's Computational & Information Systems Laboratory (CISL)
🌏 Open-source contributor to Xarray, CuPy-Xarray, Zarr-Python, WRF, CESM/CTSM, and Project Pythia
⚙️ Architecting and optimizing distributed multi-node, multi-GPU training infrastructure on NCAR's supercomputers using PyTorch and JAX
📊 Building GPU-native data pipelines for petabyte-scale Earth system datasets
🌱 Contributing to the Pangeo ecosystem and teaching scalable geospatial data analysis at SciPy, ESDS, and NCAR workshops
💬 Ask me about AI/ML for weather and climate, optimizing AI workflows, distributed training on HPC, and scalable geospatial data workflows
📫 Find me on LinkedIn
😄 Pronouns: she/her/hers