Research Associate - Applied Data Scientist
Richland, Washington, United States
Successfully designed and executed a project that utilized unsupervised machine learning to identify the Raman spectra of certain phenolic compounds using Surface Enhanced Raman Spectroscopy (SERS) and supervised machine learning to quantify those compounds once identified. This pioneering work clearly demonstrates that SERS combined with machine learning can be used to model small organic molecules such as phenolics. Successfully designed and executed a project for tracking alcoholic fermentation of red and white wines using spontaneous Raman spectroscopy (SRS) and quantitative machine learning algorithms. This methodology greatly simplifies and expedites accurate tracking of fermenting wines by exposing said wines to a non-destructive laser beam and recording their subsequent Raman spectra. Successfully designed and executed a project focussed on quickly and accurately predicting the phenolic content of red wine by recording their UV-visible spectra. This work led to the development and deployment of Shiny apps using the Posit’s Shiny framework in commercial wineries in Washington State and California. With the apps, anyone can accurately predict the concentration phenolic compounds in their wine regardless of their level of expertise.Routinely mentored graduate students, edited graduate theses and manuscripts.Handled purchasing and reconciliation.Routinely analyzed and visualized data using the R and Python languages.Created posters and other media to gain interest in awarded grants and attract certificate students and participate in workshops.Wrote and edited manuscripts and submitted them for publication for my own work as well as other projects I was directly involved with.
