About me.

As a PhD in STEM, I bring over 15+ years of experience with a strong foundation in data analysis, critical thinking, and statistical modeling. My expertise lies in interpreting large and complex datasets to uncover meaningful patterns and generate actionable insights—with results published in peer-reviewed scientific journals.

I apply deep statistical knowledge to choose and implement the right tools for the problem at hand—whether it's detecting trends in noisy time series, modeling complex environmental systems, or validating experimental data. From regression analysis and time-series decomposition to probabilistic modeling and uncertainty quantification, I focus on methods that provide clarity, accuracy, and relevance.

I leverage MATLAB for high-efficiency data processing and visualization, particularly with large, high-resolution datasets. My projects have included redefining seasonal patterns in Antarctica's McMurdo Dry Valleys using 30 years of environmental data collected at 15-minute intervals, as well as analyzing high-frequency (1 kHz) data from a large-scale experimental flume facility.

This combination of scientific rigor, statistical expertise, and computational skill allows me to deliver high-value insights to clients and collaborators across academia, industry, and environmental consulting—whether for research, decision-making, or system optimization.