This is my Story

I'm a graduate student in Data Science at Willamette University, where I also earned a B.S. in 2024 with a minor in Biology. Since 2022, I've worked in Briana Lindh's bee lab studying Oregon bees, and we are currently collaborating on my capstone research project.
In Summer 2024, I collected specimens in Montana's remote Hi-Line for Casey Delphia's Wild Bees of Montana Project at MSU - Bozeman. My current focus is on developing tools to streamline Professor Lindh's future research. Using computer vision, bioinformatics, species distribution modeling, and more, we will gain a better understanding of the specimens already in our inventory, methods to improve capture of new specimens, and generate new insights into the genetic, morphological, and ecological connections they all share.
My goal is to bridge traditional methods of field biology with modern computational tools to better understand native pollinator ecologies, especially as they face increasing pressure from climate change, habitat loss, invasive species, and other factors. By synthesizing these diverse datasets, I hope to help document part of Earth's miraculous biodiversity before any more of it is irreversibly lost.

Programming Languages

Languages I use for data analysis and database queries.

Workflow and Development

Tools I use for writing and versioning code.

Data and Visualization Tools

Tools I use for data exploration and visualization.

Productivity

Tools I use for communicating findings.

Featured Projects

Data Science Capstone - Melissodes Classification

For my capstone project, I am working to construct a data pipeline that will simplify future research efforts for Professor Briana Lindh's bee lab. This project aims to unite several very different types of data, such as morphological patterns in wing venation and facial coloration, genetic relationships based on COI mtDNA, and the type of plant that each of our specimens was caught on. By combining all of these methods of identification, it will not only streamline the process of finding individual specimens in our massive collection, but it will also speed up comparisons between these specimens, hopefully leading to a well-substantiated ID in much less time.

Coming Soon!

Predicting Wildfires in the Mojave Desert

While studying Joshua Trees with Willamette's Smith Lab in Summer 2023, I used data science to predict the areas where wildfires and the invasive grass species that exacerbate them were most likely to occur in the Mojave desert. The results were generated using publicly available data that described the climate, plant occurences, and which areas had previously been subjected to major wildfires in the Mojave Desert. I generated probability maps that highlighted the wilfire-affected areas with the most conducive conditions for a given plant species. By comparing the overlapping area with survey data for thousands of Joshua trees, I showed which invasive plant species posed the greatest threat to the remaining Joshua trees by driving wildfires in their limited range. My research was selected to represent Willamette's Life Sciences department at the annual Murdock Conference.

Slideshow Link Interactive Project Page

Photodex.io

I host and maintain this website that allows anyone to grab pictures of Pokemon from an album on flickr. It then arranges those pictures into a Pokedex format, with blank spaces for every creature they have yet to photograph. It is intended to be used for AR Snapshots in Pokemon GO, like those pictured to the right. Recently, a few users have started uploading crafts, such as needlefelted or pipe cleaner Pokemon.

Check it out!

Super Smash Bros Visualization

I worked together with my housemates to analyze the properties of different characters in our favorite fighting game, Super Smash Bros. Ultimate. We worked to find ways for a user to pick characters that would have an advantage in an upcoming match. Based on the results of millions of competetive matches, we created a shiny app that lets a user select their opponent's character. It then displays a selection of characters that have the best track record, as well as the stages that character experiences the highest rates of victory on. We also produced several clean and informative visualizations.

Slideshow Link Shiny App Link

RYAN STROBEL DATA SCIENTIST • INSECT ENTHUSIAST