• I received my PhD in Civil and Environmental Engineering from Carnegie Mellon University. My graduate work focused on the classification and quantification of engineered nanomaterials in soils using high-dimensional chemical datasets and machine learning methods. I also developed methods to classify metal-based particles in particulate matter in the atmosphere.
• Currently, I am a Postdoctoral Scholar at UC San Francisco in the OB/GYN Department. My research at UCSF focuses on identifying correlations of exogenous compounds with endogenous metabolites measured by High Resolution Mass Spectrometry (HRMS) Data and determine how that impacts adverse health outcomes in pregnant women and their children. I am also incorporating bioinformatics into my research to identify biomarkers that are associated with adverse health effects.
• My graduate educational background comes from the environmental forensics/chemistry field, and I have a strong interest towards applying statistical and machine learning methods to chemical/biological datasets. I have experience coding in Python with a specialty in Pandas, NumPy, Scikit Learn, PyTorch, Seaborn, and Scipy.