Backend and data platforms built for seasonal scale and operational clarity.

Problem

Recommendation and modeling teams need reliable access to agricultural models and geospatial data across continental operations, with demand that rises sharply during seasonal production windows.

Approach

Architect backend and data services across AWS and GCP, shape versioned REST contracts, lead a WunderGraph-based federated GraphQL platform, and connect BigQuery data workflows with production telemetry and operational controls.

Impact

The platform supports model execution and geospatial data delivery across millions of acres annually. Backward-compatible contracts, schema governance, and reliability practices keep high-concurrency seasonal workloads predictable and observable.

Problem

Researchers needed a dependable way to submit, coordinate, and reproduce heterogeneous biological simulations without manually managing solver environments or HPC job lifecycles.

Approach

Built SLURM-based dispatch and backend integration for simulation setup, metadata, workload control, lifecycle status, and result retrieval. Containerized heterogeneous solver environments and used messaging to coordinate long-running distributed work.

Impact

The infrastructure enabled reproducible execution across VCell, BioSimulations, and BioSimulators and contributed to two peer-reviewed Nucleic Acids Research publications.

Problem

Agricultural suitability analysis is difficult to trust when preprocessing, assumptions, validation choices, and data lineage are not captured consistently.

Approach

Created a configuration-driven workflow with deterministic fingerprints, provenance manifests, spatial cross-validation, and sensitivity analysis across raster and climate inputs.

Impact

The project makes agricultural modeling experiments traceable, repeatable, and easier to evaluate across changing datasets and regions.