My experience as an analyst with ITA has taken many analytical roles, such as an Operations Researcher, Data Scientist, and Data Engineer. I have built mission execution and reporting tools and apps, pricing/performance models, and staffing optimization models in support of Commander, Navy Installations Command; Applied calibrated opinion pooling algorithms across classification prediction distributions to boost predictive power in support of an insider threat detection program for IARPA; Developed Bayesian networks with application to suicide analysis, infrastructure resiliency, and water-pipe networks; and have delivered technical training and taught professional short courses. These analyses require data mining, machine learning, applied statistics, modeling & simulation, database and spreadsheet development, risk & decision analysis, data visualization, and cost-benefit analysis. Through my experience and coursework I am an experienced programmer using Visual Basic for Applications in Microsoft Access and Excel, Python, SQL, R, Netica, Tableau, HTML, JavaScript, PHP, Alteryx, D3, ExtendSim, and GIS. I am also passionate about human-centered design thinking and prototyping, facilitation, technical writing, training, and improving decision confidence and visibility.

Starting in the Spring of my sophomore year at JHU, I worked part-time as a researcher for the Environmental Engineering department studying risk analysis with application to statistical modeling of water main breaks. This NSF funded project focused on predicting future likelihood of water main breaks by employing predictive models in absence of pipe-feature data. Several supervised and unsupervised algorithms were tested and we found that although accuracy suffers from the lack of pipe-feature data, statistical modeling provides useful information for prioritization of high-risk regions for utility management. The primary software used were R and ArcGIS.In the Summer of 2013 I received a NSF grant to conduct wind energy research at the Danish Technical University in Denmark. The main goal for this 2-month project was to understand and forecast wind dynamics over the Netherlands for wind farm site planning. My research combined Wavelet Decomposition and autoregressive-moving-average (ARMA) models to forecast wind dynamics. The primary software used were R and MATLAB.