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EDUCATION

Master of Environmental Data Science and Management

(June 2023)

Bren School Of Environmental Science & Management - University of California, Santa Barbara

Highlighted Coursework: Machine Learning in Environmental Science, Databases and Data Management, Modeling Environmental Systems, Statistics for Environmental Data Science (All to be completed by June 2023)

Bachelor of Science in Earth Science – Geohydrology Emphasis

Bachelor of Arts in Geography – Geographic Information Systems (GIS) Emphasis

(June 2022)

University of California, Santa Barbara

Honors/Awards: UCSB Scholarship, Outstanding Achievement in the Geography Major

Highlighted Coursework:

Remote Sensing- Advanced image processing, including data fusion and resampling techniques, atmospheric corrections, global navigation satellite systems, and hyperspatial sensors with emphasis on applications.

Ocean Remote Sensing- Physical principles and tools required for processing active and passive remote sensing data for coastal and ocean applications. Topics include remote sensing of phytoplankton, sea surface temperature, ocean winds and currents, and sea ice. Lab includes analysis of optical, thermal infrared, passive microwave, and radar data.

Awarded UCSB Scholarship, Outstanding Achievement in the Geography Major

SKILLS

Programming: Python, R, SQL, Bash ML/AI: Deep Learning (PyTorch, Keras, CNNs), Random Forests, Time-Series Modeling, Model Deployment Geospatial & Remote Sensing: Google Earth Engine, ArcGIS Pro, QGIS, PostGIS, Raster/Vector ETL
Cloud & Systems: Linux/Unix, Docker, AWS (S3, boto), GCP (service accounts, Earth Engine) Databases: PostgreSQL/PostGIS, SQLite
Other Tools: Git/GitHub, VSCode, Jupyter, RStudio, Quarto
Languages: English, Spanish (fluent)
Clearances: Eligible for U.S. security clearance

TECHNICAL EXPERIENCE

Data Scientist - SIG-NAL (Spatial Informatics Group – Natural Assets Lab), Remote, (10/23 – Present)

  • Led design and deployment of a NASA-funded wildfire mitigation toolkit ($171K), developing and integrating 300+ pages of domain guidance, 110 StoryMaps, and interactive ESRI Experiences for community and agency use.

  • Rebuilt a legacy environmental modeling system in Python, transforming it into a reliable production framework for wildfire scenario simulations while eliminating $169K in contractor reliance.

  • Engineered and deployed pipelines in Python and Google Earth Engine to automate ingestion and preprocessing of satellite imagery and climate datasets at regional-to-national scale, enabling repeatable, production-ready workflows for wildfire and climate resilience analysis.

  • Managed geospatial cloud infrastructure and served as ESRI administrator, overseeing user access, content publishing, and creation of public-facing assets to support federal, regional, and local stakeholders.

Air Quality/Google Earth Engine Intern – Universities Space Research Association (USRA), Remote, (08/23 - 12/23)

  • Developed a Google Earth Engine–powered pipeline to preprocess MERRA-2 climate reanalysis and satellite datasets for integration with a pre-trained CNN model predicting PM2.5 air quality.

  • Engineered scalable preprocessing scripts for fetching and normalizing geospatial data, improving model input accuracy and supporting real-time predictions in data-limited regions.

  • Deployed the pipeline in a Python-based web application on Hugging Face, delivering an interactive, production-ready interface that enhanced data accessibility for researchers and public stakeholders.

Master’s Capstone Project - Informing Forest Conservation Regulations in Paraguay (1/22–Present)

Client: Paraguay — National Forestry Institute; UCSB — Dr. Robert Heilmayr | Role: Machine Learning Engineer

  • Applied machine learning techniques by creating a Random Forest model in Python to predict future deforestation patterns and generate pixel-wise probabilities of imminent deforestation.

  • Developed a data acquisition and preprocessing pipeline with Google Earth Engine and Python, supporting large-scale geospatial data analysis and enhancing the accuracy of deforestation predictions.

  • Estimated protected forest area under different regulations by developing a law-based geospatial simulation tool in R. This tool facilitated a comparison between the most and least stringent regulations, revealing a difference of 3,397,183 ha in the undeveloped Chaco region.

  • Utilized geospatial overlays for a comprehensive assessment of land use plan compliance and deforestation rates in the Paraguayan Chaco, discovering 44% of the deforestation occurred in protected areas and was considered unauthorized, totaling 21,321 ha of illegal deforestation.

  • Enhanced stakeholder engagement & decision-making by providing an interactive Shiny dashboard for examining results, serving as a crucial tool for informed policy making on forest conservation and land use.

GEOSPATIAL & DATA SCIENCE PROJECTS

Burn Severity with Sentinel-2 data using Google Earth Engine | Working with Environmental Data (12/22)

  • Conducted burn severity analysis of the August Complex Fire, utilizing Sentinel-2 Image Collection and MTBS Feature Collection.

  • Developed a processing and visualization pipeline for the difference normalized burn ratio (DNBR) by severity class using Google Earth Engine and Python.

  • Leveraged the GEE platform to efficiently process and analyze large-scale satellite data.

Statistical Analysis of NDVI in Redlined Regions | Statistics for Environmental Data Science (11/22)

  • Managed data wrangling and exploratory data analysis (EDA) in R, leading to a cleaner, more organized dataset for analysis.

  • Applied Log-Log Ordinary Least Squares Regression & hypothesis testing for a comprehensive statistical analysis of NDVI data in redlined regions, highlighting non-linear relationships and informing urban planning policies.

  • Interpreted regression coefficients to provide insights on the impact of individual variables, which can influence decisions or policies.

Analyzing Greenness through NDVI in Redlined areas in Philadelphia, PA | Undergraduate Thesis (4/22–6/22)

  • Preprocessed Landsat 8 OLI satellite data using RStudio for NDVI calculations, resulting in a comprehensive understanding of greenness levels in redlined area.

  • Conducted QGIS processing for NDVI and zonal statistics calculation, supporting the development of environmental improvement strategies.

  • Integrated census median income, NDVI, and Redline data through QGIS and Excel, providing a multifaceted view of socio-economic and environmental factors in redlined areas.

Additional Education

El Camino Community College
(June 2020)

El Camino College Foundation - Scholarship

Associate of Science in Physics for Transfer

Associate of Science in Mathematics for Transfer

Associate of Science in General Science – Honors

Associate of Arts in General Arts - Honors Associate of Arts in General Studies Biology and Physical Science