This section highlights some of my academic research and scholarly contributions in statistics and data science. From simulation-based thesis work to peer-reviewed journal reviews, each piece reflects a commitment to methodological rigor and real-world relevance. Whether presenting at conferences or reviewing cutting-edge papers, I approach research with curiosity, clarity, and a strong desire to impact decision-making.
What if your favorite font is making you dumber? This SAS-based factorial experiment explores how font type, text size, and screen vs. paper reading affect comprehension. You might never read the same again.
This research takes a hard look at the statistical tools actuaries rely on. Through simulation studies and power analysis, we expose how heavy-tailed and truncated distributions can distort goodness-of-fit results—and why blind trust in p-values may be dangerous.
Few graduate students are ever asked to review for a global statistical journal—fewer still leave their mark. Dive into my peer review journey with JSCS, where I was entrusted to critique manuscripts on deep learning in medicine, novel distributions, and high-stakes classification systems. These are more than reviews—they're rigorous dialogues at the frontier of statistical science.