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素林

@0005979

I turn open-ended problems into rigorous quantitative studies, behavioral insights, and evidence-based intervention ideas.

China

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What I'm looking for

I'm looking for opportunities in behavioral science, user research, forecasting, or evidence-based interventions where I can turn open-ended questions into rigorous quantitative studies and actionable insights.

I've led award-winning mathematical modeling research, including a National First Prize in the MathorCup Mathematical Modeling Challenge. I turned an open-ended health-management problem into a quantitative workflow for risk stratification, user segmentation, and personalized intervention design.

I built and evaluated LASSO, logistic regression, random forest, SHAP, K-means, Markov chain, and dynamic programming models, using cross-validation and sensitivity analysis to assess robustness. I also review literature, clean and analyze secondary data, and translate findings into research papers.

In MCM, I examined how generative AI could reshape occupations, education, and social equity. Through an independent UX research project, I evaluated AI learning-assistant features and identified opportunities in personalization, feedback loops, accessibility, and learner engagement.

Experience

Work history, roles, and key accomplishments

Independent UX Research Project

AI Learning Assistant Experience Evaluation

Evaluated AI-generated notes, interactive explanations, flashcards, practice, and lecture features through structured hands-on product testing. Identified opportunities in personalization, feedback loops, accessibility, and learner engagement.

UX Research Product Testing User Engagement Personalization Accessibility

Team Leader

Mathematical Contest in Modeling (MCM)

Investigated how generative AI may reshape occupations, education, and social equity by combining AI exposure, human-centered capabilities, productivity effects, and regional differences in one analytical framework. Synthesized literature and secondary data, conducted sensitivity and robustness analyses, and coordinated preparation of the team's English-language final paper.

Quantitative Modeling literature review Sensitivity Analysis Robustness Research Writing Team Coordination

Team Leader

MathorCup Mathematical Modeling Challenge

Framed an open-ended health-management problem into a quantitative workflow and built models using LASSO, logistic regression, random forests, SHAP, K-means clustering, Markov chains, and dynamic programming. Reviewed literature, cleaned and analyzed secondary data, and integrated findings into the final research paper.