Developed multiobjective genetic algorithms with agent-based simulation fitness functions to improve infrastructure restoration time and cost following natural disasters by an average of 49% and 30%, respectivelyConceptualized and implemented novel methods to improve computational and time efficiency of multiobjective genetic algorithms, achieving state-of-the-art performanceArchitected and applied deep learning models to real-world problems employing diverse input structuresModeled complex socio-technical systems using a variety of modeling and simulation techniques, enabling optimization and actionable insights Created virtual reality environments facilitating immersive visualization, decision-making, and training processes

Developed predictive algorithms using ensemble classification models to forecast and prevent recurrent service dispatches, yielding accuracy eclipsing 80%Employed mixed-integer programming to optimize facility location, minimizing expected service vehicle travelWorked with field technician managers to produce automated tools to mine, analyze, and display data and suggestions in real-time dashboardsSelected as corporation-wide contest finalist for development of productivity applet aimed at increasing employee efficiency, communication, and physiological well-being

Utilized time-series forecasting to make recommendations describing the monetary potential of client's utility usage trendsDeployed automated processes to mine and present historical client data from large, heterogenous data sources and structuresServed as liaison between Senior Engineering Team and Research/Analytics Team, ensuring thorough understanding of intent and capability