Research
Research Philosophy
My research sits at the intersection of remote sensing, machine learning, and environmental justice. I believe that the most powerful environmental science happens when we combine technical rigor with human empathy, using cutting-edge technology to address real-world challenges faced by communities around the globe.
“Every pixel in a satellite image tells a story about human interaction with the environment. My job is to teach computers to read those stories with both precision and compassion.”
- Community-Centered: All research should ultimately benefit the communities whose environments we’re studying
- Interdisciplinary: The best solutions come from combining insights across multiple fields
- Reproducible: Open science and reproducible research practices are essential for building trust
- Actionable: Research should lead to tangible policy recommendations and environmental action
Current Research Projects
Satellite-Based Irrigation Mapping in Sub-Saharan Africa
Collaborators: UCSB Water Vegetation and Society (WaVeS) Lab, Prof. Kelly Caylor
Timeline: June 2024 - Present
Status: In Progress
This project uses high-resolution satellite imagery and machine learning to identify and map irrigation patterns across Sub-Saharan Africa. Traditional remote sensing methods often miss small-scale agricultural practices, leading to significant underestimates of water use and agricultural productivity.
Key Innovation: Developing statistical frameworks and Python workflows to process multi-temporal Sentinel-2 imagery and identify center pivot and decentralized irrigation systems. The analysis integrates satellite data with hydrological and agricultural systems modeling.
Impact: Research findings are informing water resource management decisions and climate adaptation planning for local governments and agricultural extension services.
Processing Sentinel-2 (10m resolution) multi-temporal data to detect irrigation signatures across seasons. Developing Python workflows for large-scale satellite imagery analysis and spatial statistical modeling of agricultural water systems.
Proposed & In-Progress Projects
Scaling Metadata Quality Assessment for Environmental Data Repositories
Client: Arctic Data Center / National Center for Ecological Analysis and Synthesis (NCEAS)
Timeline: Fall 2024 - Spring 2025
Status: MEDS Capstone Project
This capstone project builds reproducible, scalable workflows to aggregate and visualize FAIR (Findable, Accessible, Interoperable, Reusable) metadata quality assessments across environmental data repositories.
Project Goal: Currently, the Arctic Data Center runs automated metadata checks generating per-dataset results, but aggregated results are not readily available. With thousands of datasets, it’s difficult to identify systemic curation issues such as missing ORCIDs or inconsistent units. This project transforms individual metadata quality assessments into standardized, scalable, and actionable insights.
Key Deliverables:
- Repeatable ingest pipeline - Clean, timestamped snapshots of quality assessment data enriched with dataset metadata, with validation to catch data format changes early
- Automated visualizations and analysis - Generate FAIR pillar summaries, temporal trends, and first vs. latest comparisons that auto-update when new data arrives
- Focused deep-dives - Analyze specific dataset types or disciplines, regroup individual checks into themes, and track percent-pass and patterns across time
- Lightweight access interface - Simple filterable web interface to view and download tables, figures, and summaries by time, metadata standard, pillar, or dataset type
- Stretch goal - Configure one additional repository with the same framework to demonstrate portability
Technical Approach: Building reproducible workflows in Python with open data formats (CSV, Parquet). Using Quarto/Jupyter for reports. Emphasis on lightweight, maintainable design that repository staff can sustain post-project.
Impact: This project enhances Arctic research data accessibility and usability by turning existing checks into clear, repeatable quality signals. Plain-language, FAIR-aligned metrics reduce barriers for research teams, particularly those with limited institutional data support, promoting equitable participation in environmental science. The scalable framework offers a model for other repositories, improving the connected environmental data ecosystem.
Plain-language metrics and transparent interface aids environmental education and literacy. Cross-repository comparisons can identify patterns for new checks or standard improvements. The reproducible design fosters collaboration among repository teams and advances community standards for FAIR data.
Presentations
Conference Presentations & Talks
American Geophysical Union (AGU) Fall Meeting 2024 - “Analyzing the sustainability and climate resilience of rapidly expanding center pivot Irrigation in Sub-Saharan Africa using remote sensing” with Boser A, Perez J, and Prof. Caylor K (Washington D.C., December 9-13, 2024)
Mantell Symposium in Environmental Justice and Conservation Innovation 2024 - “Water Source Attribution for Center Pivot Irrigation in Sub-Saharan Africa” (UCSB, October 24, 2024)
Summer@Bren 2024 Flash Talks - “Satellite Data Reveal Emerging Decentralized Irrigation Systems in Sub-Saharan Africa” (UCSB, August 29, 2024)
Technical Skills & Tools
Remote Sensing
- Sentinel-1/2, Landsat, Planet imagery
- Google Earth Engine
- QGIS, ArcGIS Pro
- Rasterio, GDAL
Machine Learning
- PyTorch, TensorFlow
- Custom CNN architectures
- Time series analysis
- Transfer learning
Programming
- Python (pandas, numpy, scikit-learn)
- R (tidyverse, spatial packages)
- JavaScript (Google Earth Engine)
- Git version control
Data Visualization
- Matplotlib, Seaborn
- Plotly, D3.js
- Tableau, PowerBI
- Custom web visualizations
Academic Background
Master of Environmental Data Science (MEDS)
University of California, Santa Barbara – Bren School
Expected June 2026 | GPA: 4.0
Focus: Remote sensing, machine learning, and environmental data engineering
Bachelor of Science in Mathematics
University of California, Santa Barbara
June 2025 | GPA: 3.67
Focus: Applied mathematics and environmental science
Associate of Science in Mathematics
Sacramento City College
June 2022 | GPA: 4.0
Recognition
- MEDS Student Faculty Representative - Selected by MEDS cohort to represent student voice in faculty meetings and curriculum development
- Bren Environmental Leadership Fellow - Competitive fellowship supporting research on irrigation systems and water resource sustainability in Sub-Saharan Africa (June-December 2024)
Research Collaborations
I believe that the best research happens through collaboration. I work with researchers across multiple disciplines, academic institutions, and community partners.
Academic Partners
- UCSB Water Vegetation and Society (WaVeS) Lab
- UCSB Bren School of Environmental Science & Management
- Arctic Data Center / National Center for Ecological Analysis and Synthesis (NCEAS)
Community & Professional Partners
- IV Recovery Community Initiative (Founder)
- Women in Science and Engineering (WiSE) Mentorship Program
Research Impact
My work focuses on generating actionable insights from environmental data that can inform policy and conservation decisions.
- Water Resource Sustainability - Analyzing irrigation systems in Sub-Saharan Africa to inform climate adaptation and water management strategies
- Environmental Data Infrastructure - Building tools to improve metadata quality and accessibility across environmental research repositories
- Community Engagement - Organizing local environmental stewardship through the IV Recovery Community Initiative
Let’s Collaborate!
I’m always excited to collaborate with researchers, practitioners, and communities who share my passion for using technology to address environmental challenges. Whether you’re interested in remote sensing, machine learning, environmental justice, or the intersection of all three, let’s connect!