Demo Data from SIGIR 2026 / reviewers in the wild / expert
Hanxiu 'Hazel' Zhu
dblp:340/6879
· DBLP profile ↗
5ranked-venue papers
2as first author
5since 2021 · last 2025
0000-0003-0777-6090ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 5 · 2 first-author · 5 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
3 papers |
Accessibility and assistive technology · 71% Health and well-being technologies · 23% Design research and methods · 7% |
Topics — the 5 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Health and well-being technologies
vocal training |
0.9 | 1 | 2025 | Beyond the "Industry Standard": Focusing Gender-Affirming Voice Training Technologies on Individualized Goal Exploration · CHI 2025 |
Accessibility and assistive technology › software accessibility
game accessibility |
0.8 | 1 | 2024 | Surveyor: Facilitating Discovery Within Video Games for Blind and Low Vision Players · CHI 2024 |
Design research and methods
participatory design |
0.3 | 1 | 2025 | Beyond the "Industry Standard": Focusing Gender-Affirming Voice Training Technologies on Individualized Goal Exploration · CHI 2025 |
Accessibility and assistive technology
assistive navigation |
0.2 | 1 | 2024 | Surveyor: Facilitating Discovery Within Video Games for Blind and Low Vision Players · CHI 2024 |
Accessibility and assistive technology › visual impairment
blind and low vision users |
0.2 | 1 | 2023 | ImageAssist: Tools for Enhancing Touchscreen-Based Image Exploration Systems for Blind and Low Vision Users · CHI 2023 |
Methods — techniques the papers use, named apart from their topics
formative study · 0.8comparative evaluation · 0.8user study · 0.7design study · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | FocusView: Understanding and Customizing Informational Video Watching Experiences for Viewers with ADHDabstractWhile videos have become increasingly prevalent in delivering information across different educational and professional contexts, individuals with ADHD often face attention challenges when watching informational videos due to the dynamic, multimodal, yet potentially distracting video elements. To understand and address this critical challenge, we designed FocusView, a video customization interface that allows viewers with ADHD to customize informational videos from different aspects. We evaluated FocusView with 12 participants with ADHD and found that FocusView significantly improved the viewability of videos by reducing distractions. Through the study, we uncovered participants' diverse perceptions of video distractions (e.g., background music as a distraction vs. stimulation boost) and their customization preferences, highlighting unique ADHD-relevant needs in designing video customization interfaces (e.g., reducing the number of options to avoid distraction caused by customization itself). We further derived design considerations for future video customization systems for the ADHD community. Hanxiu 'Hazel' Zhu, Ruijia Chen, Yuhang Zhao 0001 |
ASSETS | 1 |
| 2025 | Characterizing Collective Efforts in Content Sharing and Quality Control for ADHD-relevant Content on Video-sharing Platforms
Hanxiu 'Hazel' Zhu, Avanthika Senthil Kumar, Sihang Zhao, Ru Wang 0002, Xin Tong 0004, Yuhang Zhao 0001 |
ASSETS | 1 |
| 2025 | Beyond the "Industry Standard": Focusing Gender-Affirming Voice Training Technologies on Individualized Goal Exploration
Kassie Povinelli, Hanxiu 'Hazel' Zhu, Yuhang Zhao 0001 |
CHI | 2 |
| 2024 | Surveyor: Facilitating Discovery Within Video Games for Blind and Low Vision PlayersabstractVideo games are increasingly accessible to blind and low vision (BLV) players, yet many aspects remain inaccessible. One aspect is the joy players feel when they explore environments and make new discoveries, which is integral to many games. Sighted players experience discovery by surveying environments and identifying unexplored areas. Current accessibility tools, however, guide BLV players directly to items and places, robbing them of that experience. Thus, a crucial challenge is to develop navigation assistance tools that also foster exploration and discovery. To address this challenge, we propose the concept of exploration assistance in games and design Surveyor, an in-game exploration assistance tool that enhances discovery by tracking where BLV players look and highlighting unexplored areas. We designed Surveyor using insights from a formative study and compared Surveyor’s effectiveness to approaches found in existing accessible games. Our findings reveal implications for facilitating richer play experiences for BLV users within games. Vishnu Nair, Hanxiu 'Hazel' Zhu, Peize Song, Jizhong Wang, Brian A. Smith 0001 |
CHI | 2 |
| 2023 | ImageAssist: Tools for Enhancing Touchscreen-Based Image Exploration Systems for Blind and Low Vision UsersabstractBlind and low vision (BLV) users often rely on alt text to understand what a digital image is showing. However, recent research has investigated how touch-based image exploration on touchscreens can supplement alt text. Touchscreen-based image exploration systems allow BLV users to deeply understand images while granting a strong sense of agency. Yet, prior work has found that these systems require a lot of effort to use, and little work has been done to explore these systems’ bottlenecks on a deeper level and propose solutions to these issues. To address this, we present ImageAssist, a set of three tools that assist BLV users through the process of exploring images by touch — scaffolding the exploration process. We perform a series of studies with BLV users to design and evaluate ImageAssist, and our findings reveal several implications for image exploration tools for BLV users. Vishnu Nair, Hanxiu 'Hazel' Zhu, Brian A. Smith 0001 |
CHI | 2 |