Seattle, Washington, United States
•Improved accessibility and equity in programming assessments at UW by investigating the impact of oral coding interviews, proposing scaffolding techniques, and integrating feedback from TAs and students, resulting in revised practices that better support English language learners and students with diverse abilities. •Co-authored a peer-reviewed paper accepted at RESPECT 2025 (IEEE/ACM), contributing to study design, data analysis, and manuscript writing, influencing broader discourse on equitable computing education. •Presented project findings with Professor Kevin Lin at the Consortium for Computing Sciences in Colleges (CCSC-NW 2025), engaging educators in discussions on inclusive assessment design.
•Lead TA for multiple courses (INFO 180 Introduction to Data Science, INFO 201 Foundational Skills for Data Science, and INFO 498 AI, Robots, and Religion), coordinating with faculty and mentoring new TAs to ensure consistent instruction. •Conducted coding labs and review sessions on R programming, data wrangling, visualization, and statistical modeling; facilitated discussions on AI, ethics, and how emerging technologies intersect with religion, culture, and human values. •Mentored over 1,000 students through office hours, constructive feedback, and one-on-one support, fostering growth in coding proficiency and data analysis skills. •Collaborated with instructors to design grading rubrics and evaluate projects, ensuring fair and consistent feedback.
•Improved ad targeting on delivery platforms (Ele.me, Meituan) by building a logistic regression model to predict the likelihood of customer response to discounts, enabling the Ads team to apply personalized promotions and resulting in a ~2–5% projected increase in campaign conversion rate. •Identified high-potential customer segments by analyzing historical click-through and order data and presenting actionable insights to the Ads team, who implemented a pilot multi-armed bandit strategy to optimize budget allocation and test targeting improvements. •Optimized ad creatives and limited-time offers by designing A/B tests and publishing experiment readouts, collaborating with the marketing team to adjust campaigns and measure early-stage engagement lift. •Translated data findings into segment-specific recommendations and optimized ad timing for the Ads team, supporting subsequent campaign refinements and helping guide repeat-order behavior and targeted promotions.