Hi 👋, my name is Rich.

I'm a data scientist at LWA and spend most of my time in R automating ETL pipelines for sensor networks 📡, building Shiny Apps and dashboards 🖥, designing approaches with spatial statistics and hydrologic models, and generally wrangling lots and lots of data.

I have a PhD in Hydrology and my dissertation is titled ‘Emerging consequences of regional-scale aquifer depletion: data-driven and numerical models of well failure, basin salinization, and contaminant transport’ (my exit seminar can be viewed here). Early in my PhD, I found that I really enjoyed data science and programming, and I used these years to sharpen those skills. My published research includes NLP and network analysis, spatial statistics, and physical modeling of 3D, subsurface contaminant transport.

I'm an #rstats nerd and automation/reproducibility fanatic. My favorite tools include tidyverse (dplyr, ggplot2, purrr), shiny, flexdashboard, DT, RMarkdown (for dashbaords/reporting), sf, sp, raster, leaflet (for spatial data), and DBI for databases. A few projects I'm proud of include an R package to query water quality data 📦, R data science curriculum 📚, a dashboard that makes millions of water quality observations understandable 📈, and a model that predicts the risk of wells going dry 💧 funded by Microsoft's AI for Earth Grant.

Before my PhD, I taught environmental science to middle and high school students in Yosemite and the Marin Headlands for the educational nonprofit NatureBridge. I spent summers leading expeditions in the wilderness, and in Thailand for National Geographic Student Expeditions.

In my free time, I enjoy anything that puts me in a flow state: alpine climbing 🧗🏼, running 🏃‍♂️, surfing 🏄‍♂️, tinkering on bikes 🚴‍♂️, reading 📚, playing guitar 🎸, and cooking 🧑‍🍳.