Research Scientist | Ecosystem and Species Distribution Modeler | Data Analyst
About Me
I am an environmental data scientist and quantitative ecologist with a strong foundation in coding, statistical modeling, and spatial analysis and marine ecology. My expertise lies in translating complex ecological data into actionable insights using analytical tools and programming languages. I bring hands-on experience working with large-scale environmental datasets, building reproducible workflows, and developing models to support scientific research and decision-making.
I am proficient in R and experienced in statistical modeling (GLM, GAM, delta-GLM, LME), machine learning (e.g., random forests, boosted regression trees), and advanced spatiotemporal modeling using tools like VAST, sdmTMB, and INLA. I routinely work with geospatial data using R packages such as sf, terra, and raster, and have experience with batch and parallel processing on high-performance computing systems. My background includes developing single-species stock assessments (Stock Synthesis, ADMB), building and calibrating ecosystem models in Ecopath with Ecosim (including Ecospace), and designing scenario-based simulations. I also create interactive R Shiny applications—such as RedTideVIS—and produce reproducible, publication-ready outputs using ggplot2, RMarkdown, and GitHub. Additionally, I am familiar with C++ (via TMB), Python, SQL, APIs (e.g., Global Fishing Watch), and GIS tools like QGIS.
I have contributed to a range of interdisciplinary research projects, including: (1) assessing the impacts of red tides on fish populations and fisheries in the Gulf of Mexico; (2) evaluating survey design strategies under climate-driven species shifts in the Bering Sea; (3) integrating global climate and ocean models with regional ecosystem modeling frameworks; and (4) analyzing the effectiveness of marine protected areas in the Mediterranean Sea. Across these projects—whether focused on marine ecosystems, climate change, or spatial ecology—I aim to apply robust environmental data science to inform research, resource management, and conservation efforts.