OER Integration at UC San Diego
Institutional research and infrastructure planning for Open Educational Resource adoption, translating equity goals into actionable systems recommendations.
Projects
Featured Projects
Focus: Machine Learning & Systems Engineering for Space Applications
These projects demonstrate the core skills required for ML engineering in the space industry:
- Production Systems, Building reliable, maintainable automation pipelines
- Scientific ML, Working with simulation-based training and data scarcity
- Rigorous Analysis, Statistical validation and reproducible data science
- Systems Thinking, Infrastructure design and institutional integration
Each project is designed to show not just technical capability, but engineering judgment: knowing when to automate vs. keep humans in the loop, how to validate results when ground truth is uncertain, and how to build systems that others can maintain.
What These Projects Show
Together, these projects demonstrate that I can:
- Build production systems (Newsletter Automation)
- Design ML pipelines under data scarcity (JWST Classifier)
- Perform rigorous, large-scale analysis (Renewable Energy ROI)
- Think at the institutional / system level (OER Integration)
This combination is exactly what space-industry ML teams need: strong technical skills plus the ability to navigate real-world constraints, stakeholder needs, and operational complexity.
JWST Gas Morphology Classifier for Binary Evolution
PyTorch-based CNN pipeline for classifying gas structures around binary stars using simulated JWST observations, achieving 92% accuracy with physics-informed...
Astronomy Club Newsletter Automation
Fully automated communications pipeline with LLM-based news curation, weather forecasting, and event aggregation, designed for production reliability and ope...
Renewable Energy ROI Analysis Across U.S. States
Large-scale data integration pipeline evaluating economic ROI of state-level renewable energy transitions, combining federal datasets with custom feasibility...