About me¶
I'm an HPC consultant and scientific software developer working at the intersection of AI/ML, HPC, and Earth system science 🌎. My work focuses on making GPU clusters useful for weather and climate researchers: building the distributed training systems, data pipelines, and optimization workflows that turn HPC hardware into scientific results.
Currently serving as the Technical Lead of the NSF-NCAR Community AI Ecosystem initiative, I coordinate eight labs and 50+ stakeholders to build unified geospatial AI/ML infrastructure for Earth system science.
My background spans numerical weather prediction, distributed GPU training, performance optimization on HPC/Cloud architectures, and building scalable data workflows for large geospatial datasets. I'm passionate about open science and fostering community-driven computational geoscience.
I'm an active open-source contributor and technical leader in the Pangeo ecosystem, serving as a core contributor to Xarray, CuPy-Xarray, and Pythia. I enjoy teaching and have delivered tutorials at SciPy, ESDS, and NCAR on topics ranging from scalable geospatial data analysis using Dask to distributed AI/ML workflows.
I hold a Ph.D. in Chemical Engineering from the University of Iowa, where my research focused on atmospheric chemistry modeling, performance analysis, and optimization of weather and air quality models.
Portfolio¶
Scaling AI/ML Workflows on HPC¶
Scaling and optimizing AI/ML training workflows for geoscientific applications on HPC systems.
GPU-Native Data Loading with Zarr¶
End-to-end GPU data pipeline using Xarray, kvikIO, and CuPy for accelerated AI/ML geoscientific workflows.
Dask on NCAR HPC Workshop¶
Comprehensive workshop on scalable data analysis with Dask and Xarray on NCAR HPC systems.
Workshop Website
Workshop Video - Part 1
Workshop Video - Part 2
CREDIT¶
Community Research Earth Digital Intelligence Twin — an AI-powered framework for weather and climate prediction.
CuPy-Xarray¶
Tutorial on GPU-accelerated array computing with CuPy-Xarray for geoscientific data analysis.
Distributed Training for ESS on NCAR HPC¶
Comprehensive guidelines for multi-node multi-gpu distributed deep learning training for Earth system science applications on NCAR HPC.
Kubernetes for Climate Data Visualizations¶
Harnessing Kubernetes to build scalable, interactive visualization platforms for climate data.
Deep Learning for Cloud Microphysics¶
Deep learning emulation of bin microphysics autoconversion processes as an alternative to empirical parameterizations in climate models.