US +1(865) 801-7068 | alice.yu.sun@gmail.com | https://alicesunwonder.land
Summary
Master of Information graduate from the University of Michigan with focus in user experience research and design. I explore how people think, feel, and make decisions when interacting with digital systems, drawing on both behavioral insights and user-centered design methods. And I bring experience conducting user interviews, usability testing, and synthesizing findings to guide design in collaboration with cross-functional teams.
Education
University of Michigan
Sept 2023 – May 2025
University of California San Diego
Sept 2020 – Sept 2022
Experience
Jan 2025 – Apr 2025
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Collaborated with a cross-functional team of 5 to create an AI-powered vehicle dashboard specifically to reduce cognitive load and increase on-road safety for drivers with chronic visual impairments.
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Conducted literature reviews and competitive analyses to identify gaps and opportunities in existing accessibility features. Gathered user insights through 6 interviews and 15 surveys to set design goals.
- Led usability testing with 30 participants and synthesized findings that informed final revisions, enhancing usability and safety for drivers with chronic visual impairments.
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Translated 5 key research insights into actionable design recommendations, partnering with designers to iterate wireframes and interactive prototypes that implemented traffic signal prediction, smart tailgate notification, and driver assistant cluster with legible and minimalist design.
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Led user research for a 5-member team in collaboration with Pink Fund, a nonprofit supporting breast cancer patients, to identify barriers in the online donation process.
- Designed and distributed surveys to 200+ users and conducted 20 in-depth interviews to uncover pain points around fund transparency, form complexity, and trust signals.
- Created 5 personas and detailed interaction maps to synthesize user needs and behavioral patterns.
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Conducted and analyzed usability tests with 30 participants; delivered evidence-based recommendations to improve donation flow clarity and completion.
- Increased unaided donation completion rate from 75% to 90% (n = 200 sessions, p < .01), and reduced average completion time by 42%, demonstrating measurable gains in efficiency, clarity, and user trust after implementing research-driven design changes.
Skills
Tools: Figma, Axure, Qualtrics, Python, HTML/CSS, Adobe Illustrator, Adobe Photoshop, Adobe Premiere Pro
Skills: User Research, Usability Testing, Heuristic Evaluation, Wireframing, Information Architecture