Lotus Zhang

Lotus Zhang is a postdoctoral researcher at the University of Michigan and holds a Ph.D. in Human Centered Design & Engineering from the University of Washington. Her research in Human-Computer Interaction (HCI) supports accessible creative and recreational activities across abilities and stages of life. Grounded in human-centered approaches, her work bridges technical innovation with the lived experiences of marginalized users to enable more equitable digital participation and expression.

Lotus uses mixed methods, multimodal design, and deployment studies in her research. Her dissertation focused on the blind and low vision (BLV) community, where she designed and evaluated multimodal, AI-assisted interaction techniques that make visual content authoring tools more accessible. In her current postdoctoral work with Dr. Robin Brewer, she studies the long-term, in-the-field effects of novel audio interaction designs that help older adults use voice and AI interfaces to seek information.

Lotus also brings an applied perspective from user research roles at major technology companies, where she shaped product strategy around accessibility, content safety, and human-centered AI. Her work appears in leading HCI venues, including ACM CHI, ASSETS, and CSCW. She is currently on the market for tenure-track faculty, applied/research scientist, and UX/product research roles.

Dissertation title

Accessible Digital Content Creation by Blind and Low Vision Individuals

Dissertation abstract

Creative activities are central to cultural participation, self-expression, and professional life, yet they remain largely inaccessible to blind and low vision (BLV) individuals. Most creative tools assume a sighted creator by default, offering limited nonvisual feedback to support iterative decision-making with visual content. Meanwhile, accessibility research has historically positioned BLV individuals as consumers of digital media rather than creators, leaving critical gaps in our understanding of their creative needs, goals, and workflows.

This dissertation examines how BLV individuals engage in visually mediated creative work and how AI-assisted tools can better support their creative needs. Through a user-centered, iterative approach spanning four studies, it identifies the varied feedback demands that arise across creative contexts and explores how those demands can be addressed through accessible design. First, a large-scale formative study maps the landscape of BLV creative practices, revealing strong and varied interests in visual content creation that challenge common assumptions about what BLV individuals want to make, while surfacing the distinct, task-specific feedback demands these interests entail. Then, two design explorations examine AI-powered creative support for distinct creative contexts that the formative study identified as critical yet underserved: visual privacy management in photography, and expressive aesthetic configuration in visual media. Finally, a synthesis study distills visual feedback design insights for creative tasks into a framework via a systematic literature review and expert evaluation.

Together, these studies surface how BLV individuals navigate systemic exclusion and technical barriers in creative work, and demonstrate that supporting creative agency requires tools that are responsive to users' goals, rich in contextual feedback, and grounded in non-visual ways of knowing and expressing. This dissertation contributes empirical understanding of BLV creative practices, design insights for accessible AI-assisted tool development, and a practical framework that equips researchers and practitioners to support BLV individuals' full authorship across visually mediated creative contexts.

Areas of interest

Accessibility; Aging; Creativity Support; Human-centered AI; Multimodality Design

Education

PhD in Human Centered Design & Engineering, University of Washington, 2026