Demo Data from EDBT 2026 / reviewers in the wild / expert
Ashish Ajin Thomas
dblp:408/0903
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0006-8803-9891ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 2 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
1 paper |
Accessibility and assistive technology · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Accessibility and assistive technology
deaf and hard of hearing technology |
1.0 | 1 | 2026 | FAME: Exploring Expressive Facial Avatars for Lyrical and Non-Lyrical Music Visualization for d/Deaf Individuals · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
formative study · 1.0design probe · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | FAME: Exploring Expressive Facial Avatars for Lyrical and Non-Lyrical Music Visualization for d/Deaf Individualsabstractd/Deaf and Hard of Hearing (DHH) individuals often engage with music through a multimodal approach, where visual modalities are also used rather than relying on sound alone. While tools like captions and visualizers offer partial support, they often fail to capture the emotional depth and structural nuances of music. To explore new possibilities, we adopted an iterative, probe-based approach. Through a formative study with 9 DHH participants, we identified key design requirements for visualizing rhythm, emotion, and lyrics. We developed FAME (Facial Avatar for Musical Expression), a design probe that conveys music through expressive facial animation, instrument highlights, and synchronized captions, lip-syncing to lyrics or scat-singing to melodies. Through a two-phase exploratory study with 12 DHH users, we examined FAME’s efficacy, applicability, and requirements for representing musical elements. Our findings refine design requirements for avatar-based systems and highlight the potential of avatars as expressive and socially meaningful tools for music accessibility. Suhyeon Yoo, Yifang Pan, Ashish Ajin Thomas, Karan Singh 0004, Khai N. Truong |
CHI | 3 |
| 2025 | Nabokov's Cards: An AI Assisted Prewriting System to Support Bottom-Up Creative Writing
Dashiel Carrera, Ashish Ajin Thomas, Daniel J. Wigdor |
Creativity & Cognition | 3 |