University College London (UCL)

Institute of Health Informatics

2021-present

As a health data scientist, I contribute to the development and implementation of 300+ disease phenotypes in the longitudinal research studies UK Biobank and Genes & Health to facilitate drug discovery with geneticists at GlaxoSmithKline (GSK). See more here.

Concurrently, I contribute to the Health Data Research UK-funded 'Disease Atlas' of ~7,000 conditions by assessing the prevalence of each disease in public data sources, like clinical trials and publications. This work will identify over- or under-studied diseases based on public health impact. See more here.

2020-2021 

Using longitudinal electronic health records from almost 500,000 people in England, I investigated prescribing patterns of primary care doctors for COVID-positive patients during the first two waves of the pandemic. This work granted me a Master of Science in Health Data Science with Distinction.

Mount Holyoke College (MHC)

2019-present

I maintain Research Affiliate status to remotely continue investigations using laser-induced breakdown spectroscopy (LIBS), x-ray fluorescence (XRF), and X-ray absorption (XAS) data collected and stored at the college. My research has focused on fundamental influences to spectroscopy calibration quality, such as data set size and diversity, submodelling, machine learning method selection, effects of preprocessing, and limits of quantification.

2018-2019

I continued my undergraduate spectroscopy research as laboratory technician, analyzing and studying spectroscopy data for various techniques and applications in diverse, collaborative projects. During this time, I matured my expertise in machine learning methods, quantitative calibration optimization, portable spectroscopy instruments, LIBS data from Mars, and the complexities of studying geological standards.

2016-2018

My research career began in Darby Dyar's lab, developing diverse calibration models from a suite of 3,000+ standards and a LIBS instrument built to replicate ChemCam on Mars rover Curiosity. Machine learning analyses of these data generated two peer-reviewed, first-authored studies quantifying light elements in rocks with LIBS under different environments, and culminated in an undergraduate thesis which recieved High Honors.

University of Tennessee, Knoxville

2019-present

I collaborate and consult on research projects in the laboratory of Molly McCanta at the Department of Earth and Planetary Sciences for the appropriate usage and interpretation of machine learning models.

NASA VERITAS Science Team

2023-2026

I have received a three-year appointment on the science team for NASA JPL mission to Venus, VERITAS. For the mission, I will be developing calibrations for the Venus Emissivity Mapper (VEM) instrument to predict iron content and differentiate between rock types on the planet's surface. 

Planetary Science Institute (PSI)

2022-2025

I am currently hired to process and analyze XAS data of glasses and minerals. For this work I will be generating and optimizing multivariate machine learning models to best predict ferric iron in the standards.

NASA Goddard Space Flight Center (GSFC)

2018-2023

Kelsey Young and Christine Knudson of NASA GSFC approached Darby Dyar's lab to test handheld LIBS and XRF instruments' suitability for human exploration of planetary bodies by analyzing MHC's expansive geochemical database. I performed the analyses and modelling of the instruments' spectra and compared multivariate calibration prediction accuracies to those output by the instruments. 

the German Aerospace Centre (DLR)

(Deutschen Zentrums für Luft-und Raumfahrt)

2019

Working with Jörn Helbert in the Planetary Spectroscopy Laboratory at the Institute for Planetary Research and MHC's Darby Dyar, I collected data used to develop high-pressure geochemical calibrations for applications on the NASA mission to Venus, VERITAS.