Beiqianyi Li
Data-driven UX Researcher & Designer
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Conducted a complete data science workflow in R using real-world academic data from 53 Pennsylvania schools. Merged and cleaned four heterogeneous datasets, performed descriptive statistical analysis, and built three incremental regression models to identify key predictors of student academic outcomes. Synthesized findings into a professional R Markdown report with interactive visualizations built using plotly.
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Co-authored a peer-reviewed dataset paper published in many international platforms. Managed end-to-end data collection for a Human-AI interaction study — capturing 60+ hours of high-density physiological and behavioral data using EEG biometric systems. Standardized and deployed the benchmark dataset across global scientific repositories (Science Data Bank, GitHub), providing infrastructure for the broader AI research community to train and evaluate neural decoding models.
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Conducted quantitative analysis on 1,500+ customer transaction records to identify high-churn risk segments and engagement gaps. Authored a User Research Report synthesizing survey data from 50 respondents, pinpointing specific pain points around budgeting tools and session drop-off. Performed competitive benchmarking against market standards and developed a data-driven Customer Value Proposition with actionable feature recommendations. Note: this was a structured job simulation project.
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Led a mixed-methods UX research study to understand consumer hesitation around purchasing Apple Vision Pro. Designed survey instruments and in-depth interview scripts to capture multi-dimensional behavioral data, then synthesized findings into a comprehensive UX Insights Report. Key finding: users lacked confidence in real-world use cases — this directly informed the design of a 3D e-commerce solution featuring co-branded demos and personalized product customization. Prototype built and deployed on GitHub.
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Designed a mixed-methods research plan targeting Indonesian millennial banking users (ages 18–29). Structured ethnographic interview guides and focus group protocols to uncover behavioral insights around digital adoption and financial literacy gaps. Transformed qualitative findings into user personas mapping core pain points — financial discipline, complex jargon — and architected a strategic task flow with three targeted solutions: Visual Spending Breakdowns, Forced Savings Vaults, and Transparency Education Modals. Note: this was a structured job simulation project.
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