Research Portfolio
From early psychophysiological driver studies to large‑scale ML‑assisted survey analytics and org‑level research operations, this portfolio highlights a quantitative + mixed‑methods UX research practice focused on scalable insight pipelines, methodological rigor, and adoption-aware process design. Each project is framed with the STAR model to surface decision context, execution leverage, and measurable impact.
Reduced open‑ended survey analysis time ~30h → <8h and doubled throughput by deploying a hybrid ML + human‑in‑loop classifier and integrated behavioral log triangulation.
Real‑world driver stress elicitation: replicated 90% stress induction & 89% event correlation via mixed-methods instrumentation.
Framework + longitudinal HW usability evaluation driving remote redesign & metric expansion across millions of devices.
Progressive rollout of standardized repository & reporting boosted visibility and self‑serve insights org‑wide.