Applied expertise in ethnographic research, field observation, and qualitative analysis to mentor students in producing rigorous user insights.

Leveraged strong visual storytelling skills—including filmmaking, composition, and graphic design—to coach students in communicating research findings clearly and persuasively.

Demonstrated advanced ability in user empathy building and insight synthesis, guiding students to transform raw observations into actionable design opportunities.

Designed an AI driven shopping platform to reduce shopping decision fatigue and enhance user

confidence through emotionally intelligent AI-driven guidance.

Defined the target user group and uncovered core pain points through data analysis using

thematic analysis, affinity mapping, qualitative coding, and journey mapping.

Conducted mixed-method research, including a 182-response survey, semi-structured interviews, contextual inquiry, and A/B testing.

Designed and launched a cyber-psychic–style trading AI agent that increase user retention to the

platform by delivering ongoing emotional and cognitive support, scaling to 131K+ users in the first

15 days, contributing 6% of the platform's quarterly revenue.

Identified core user needs through market and behavioral research—translating insights into clear

product differentiation and opportunity areas to support design decisions.

Partnered with cross-functional teams to deliver product iteration quickly while designing revenue

aligned models such as tiered features and subscriptions.

Launched the Chessbot AI app and website by collaborating with cross-functional teams.

Led data driven version updates based on UI/UX redesign and core feature upgrade,

prioritized features, defined the product roadmap.

Built validated framework of 3 user segments, enabling personalized product strategy and

roadmap prioritization by executing mixed-methods research.

Increased user engagement by 58% through redesigning AI Coach agents, with prompt

engineering and user dimension segmentation.