About Me
I’m an environmental data scientist with a broad interest in the application of machine learning and statistical techniques to understand the natural world and solve environmental problems. I recently graduated from the Masters of Environmental Data Science (MEDS) program at the University of California, Santa Barbara. I grew up in Melbourne, Australia, where I fell in love with the natural world. I finished my undergraduate career at the University of Arkansas with degrees in Chemistry, Biology, and Environmental Science, along with minors in Mathematics and Physics. My initial interests took me into the world of Astrobiology, where the possibility of finding life beyond Earth deeply fascinated me. I was first exposed to the value of data skills when I was running Deep Earth Water (DEW) models to simulate the thermodynamics of amino acid synthesis under conditions hypothesized to exist on Enceladus, using data captured from the Cassini probe. I quickly realized that I wanted to be broadly, yet rigorously trained in data science - the techniques of which I hope to apply towards solving a wide range of environmental problems.
I tailored my experience in the MEDS program to gain experience in all aspects of the data science lifecycle, with an emphasis on machine learning and statistical methods. I have a strong foundation in a variety of machine learning models (Linear Regressions, Ridge, Lasso, KNN, Decision Trees, Random Forests, Gradient Boosting, SVMs), feature engineering, hyperparameter tuning, and statistical methods (hypothesis testing, multivariate analysis, nonlinear modeling, spatial-temporal sampling methods). I am comfortable in Python, R, and SQL. I am currently looking for a role that emphasizes the use of machine learning techniques to solve complex problems. I will be posting my projects here as I complete them. Feel free to connect with me through the links below!