The advent of modern synchrotron facilities which feature unparalleled brightness allows high-quality diffraction data to be collected in seconds, with the potential for thousands of diffraction patterns to be acquired on a time scale of hours. However, the analysis of even a single diffraction pattern for a new material of interest can be a time-consuming step in the process of materials discovery. A major focus of my work is in using my knowledge of diffraction and crystallography to create and validate automated pipelines for high through-put data collection, processing, and analysis of crystalline materials as performed at synchrotron and neutron sources. I have participated in and, in multiple instances, led powder diffraction experiments at synchrotron/neutron sources around the U.S. including the National Synchrotron Light Source II (28-ID-2), Advanced Photon Source (11-ID-B), and Spallation Neutron Source (BL-11A, BL-1B). These experiments typically involve the design of new experimental configurations for in operando data collection during heating or electrochemical cycling. Studies have generally encompassed solid state materials targeted towards applications in lithium metal batteries and heterogeneous catalysis. Additionally, I possess a working proficiency in the Python programming language, including its general-purpose scientific computing libraries (NumPy, SciPy, MatPlotLib, Pandas) and more specialized libraries centered around machine learning (TensorFlow, SciKit-Learn) and diffraction /atomic structure analysis (Pymatgen, GSASII, Blue Sky).