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About Candidate
I am a statistician and data scientist with expertise in operation research from my Industrial Engineering degree. I have experience as a statistical consultant where I assisted in statistical modelling, programming, and in the design of academic experiments. Following the completion of my MS in Applied Statistics, I entered the PhD program in Industrial Engineering where I have conducted literature reviews, processed data, and constructed dashboards for the DoD. Further, in my current program, I have engaged in simulation/optimization in the course of constructing adaptive decision making frameworks.
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Work & Experience
• Created and deployed a decision support system connecting climate model downscaling and DoD infrastructure management through collaboration with Princeton University, University of Iowa, and the National Center for Atmospheric Research • Built a dynamic adaptive decision-making framework for environmental management under deep uncertainty • Designed and optimized a water distribution system investment framework for cities with over-exploited aquifers • Developed a high-performance computing (HPC)–based optimization workflow for a large-scale gridded economic model, integrating multi-objective optimization techniques to inform management decisions across spatial and temporal scales • Created visuals in Python (Matplotlib, Seaborn, Plotly) and R for preliminary analysis and to enhance decision-making efficiency
• Contributed to data analysis in support of graduate students and faculty across departments • Provided statistical programming code to assist researchers (R, SAS) • Guided graduate researchers through the Design of Experiments (DOE) • Collaborated with researchers by using SQL in SAS to structure and analyze data for academic publications • Composed technical reports detailing project objectives and experimental design recommendations
