Rajeev Jain
Rajeev Jain
Research Software Engineer · Argonne National Laboratory · University of Chicago
I build software tools for scientific research — climate data analysis, cancer drug response prediction, and high-performance computing. 16 years at Argonne, two R&D 100 Awards, and a passion for open-source scientific software.
Projects
Open-source tools I lead or contribute to — each section is directly shareable.
UXarray
Python library for unstructured climate grid analysis
Challenge
Climate scientists working with unstructured grids (MPAS, ICON, SAM) lacked Python tools for conservative analysis that preserve integral quantities across non-uniform meshes.
My Role
Lead developer since project inception. Implemented core mathematical operators including conservative zonal averaging using Gauss-Legendre quadrature, Grid I/O readers for multiple formats (ESMF, MPAS, SCRIP, HEALPix), and testing infrastructure. Established continuous integration and regular PyPI releases.
Impact
Used by researchers at NCAR, DOE labs, and universities worldwide (205+ GitHub stars). Enables analysis of multi-petabyte climate datasets. Presented tutorials at SC24, AMS 2024, and ESDS Annual Event.
CANDLE / IMPROVE
Hyperparameter optimization for cancer drug response models
Challenge
Cancer drug response prediction models showed poor generalization across different pharmacogenomic datasets, requiring systematic benchmarking and optimization.
My Role
Built hyperparameter optimization (HPO) infrastructure and ran 10,000+ training experiments across Summit, Theta, and Cori supercomputers. Developed GitHub Actions workflows for cross-study validation. Maintained benchmarking framework and co-authored standardization guidelines.
Impact
Benchmarking framework used by 15+ researchers across the project. Results published in Briefings in Bioinformatics (2025) and presented at 20th Workflows Workshop (2025). Contributed to R&D 100 Award (2023).
FLASH-X
I/O optimization for exascale multiphysics simulations
Challenge
Checkpoint and restart operations were taking 30–50% of total runtime in billion-element FLASH-X simulations on leadership-class supercomputers.
My Role
Implemented asynchronous HDF5 I/O with Argobots for non-blocking checkpoint operations and integrated SZ3/ZFP compression. Built verification workflow with nightly baseline testing to ensure reproducibility. Enabled cross-checkpoint restart between AMReX and Paramesh solvers.
Impact
Achieved 40–70% reduction in checkpoint write times on Summit supercomputer. Compression reduced storage requirements by 50%+ with minimal accuracy loss. Published at SC24 workshop, contributed to R&D 100 Award (2022).
Recognition
CANDLE — Cancer Distributed Learning Environment for drug response prediction
FLASH-X — Multiphysics simulation software for exascale computing
International Meshing Roundtable — Reactor core mesh generation
Background
Lead developer for UXarray, FLASH-X, CANDLE/IMPROVE, MeshKit, and urban simulation projects.
Joint appointment supporting cancer and earth science research.
M.S. Computer Science — University of Chicago (2020)
M.S. Structural Engineering — Arizona State University (2009)
B.Tech Mechanical Engineering — IIT ISM Dhanbad (2006)
Contact
Open to collaborations in scientific computing, AI for health, and open-source software.