VLDB 2026 Research / reviewers in the wild / expert
Arnavi Chheda-Kothary
dblp:358/8275
· DBLP profile ↗
7ranked-venue papers
4as first author
7since 2021 · last 2026
0000-0001-8627-0412ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 7 · 4 first-author · 7 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | GeoVisA11y: An AI-based Geovisualization Question-Answering System for Screen-Reader UsersabstractGeovisualizations are powerful tools for communicating spatial information, but are inaccessible to screen-reader users. To address this limitation, we present GeoVisA11y, an LLM-based question-answering system that makes geovisualizations accessible through natural language interaction. The system supports map reading, analysis, interpretation and navigation by handling analytical, geospatial, visual, and contextual queries. Through user studies with six screen-reader users and six sighted participants, we demonstrate that GeoVisA11y effectively bridges accessibility gaps while revealing distinct interaction patterns between user groups. We contribute: (1) an open-source, accessible geovisualization system, (2) empirical findings on query and navigation differences, and (3) a dataset of geospatial queries to inform future research on accessible data visualization. Chu Li 0001, Rock Yuren Pang, Arnavi Chheda-Kothary, Ather Sharif, Henok Assalif, Jeffrey Heer, Jon Froehlich |
CHI | 3 |
| 2025 | A Demo of GeoQA^3: Towards An Accessible AI-based Question-Answering System for GeoanalyticsabstractFigure 1: We introduce GeoQA 3 , a novel accessible AI-based question-answering system for geovisualizations designed for screen-reader users.(A) Through a custom query pipeline, we combine geo-statistical analysis with an LLM to balance accuracy and performance.(B) Users can navigate the map through natural language commands or keyboard controls and (C) zoom in to view county-level data.The AI Chat system is context-aware, taking into account user interactions.See video for demonstration. Chu Li 0001, Rock Yuren Pang, Arnavi Chheda-Kothary, Ather Sharif, Henok Assalif, Jeffrey Heer, Jon Froehlich |
ASSETS | 3 |
| 2025 | "It Brought Me Joy": Opportunities for Spatial Browsing in Desktop Screen Readers
Arnavi Chheda-Kothary, Ather Sharif, David A. Rios, Brian A. Smith 0001 |
CHI | 1 |
| 2025 | ArtInsight: Enabling AI-Powered Artwork Engagement for Mixed Visual-Ability FamiliesabstractWe introduce ArtInsight, a novel AI-powered system to facilitate deeper engagement with child-created artwork in mixed visual-ability families. ArtInsight leverages large language models (LLMs) to craft a respectful and thorough initial description of a child's artwork, and provides: creative AI-generated descriptions for a vivid overview, audio recording to capture the child's own description of their artwork, and a set of AI-generated questions to facilitate discussion between blind or low-vision (BLV) family members and their children. Alongside ArtInsight, we also contribute a new rubric to score AI-generated descriptions of child-created artwork and an assessment of state-of-the-art LLMs. We evaluated ArtInsight with five groups of BLV family members and their children, and as a case study with one BLV child therapist. Our findings highlight a preference for ArtInsight's longer, artistically-tailored descriptions over those generated by existing BLV AI tools. Participants highlighted the creative description and audio recording components as most beneficial, with the former helping ``bring a picture to life'' and the latter centering the child's narrative to generate context-aware AI responses. Our findings reveal different ways that AI can be used to support art engagement, including before, during, and after interaction with the child artist, as well as expectations that BLV adults and their sighted children have about AI-powered tools. Arnavi Chheda-Kothary, Ritesh Kanchi, Chris Sanders, Kevin Xiao, Aditya Sengupta, Melanie Kneitmix, Jacob O. Wobbrock, Jon Froehlich |
IUI | 1 |
| 2024 | Engaging with Children's Artwork in Mixed Visual-Ability FamiliesabstractWe present two studies exploring how blind or low-vision (BLV) family members engage with their sighted children’s artwork, strategies to support understanding and interpretation, and the potential role of technology, such as AI, therein. Our first study involved 14 BLV individuals, and the second included five groups of BLV individuals with their children. Through semi-structured interviews with AI descriptions of children’s artwork and multi-sensory design probes, we found that BLV family members value artwork engagement as a bonding opportunity, preferring the child’s storytelling and interpretation over other nonvisual representations. Additionally, despite some inaccuracies, BLV family members felt that AI-generated descriptions could facilitate dialogue with their children and aid self-guided art discovery. We close with specific design considerations for supporting artwork engagement in mixed visual-ability families, including enabling artwork access through various methods, supporting children’s corrections of AI output, and distinctions in context vs. content and interpretation vs. description of children’s artwork. Arnavi Chheda-Kothary, Jacob O. Wobbrock, Jon Froehlich |
ASSETS | 1 |
| 2024 | AltGeoViz: Facilitating Accessible GeovisualizationabstractGeovisualizations are powerful tools for exploratory spatial analysis, enabling sighted users to discern patterns, trends, and relationships within geographic data. However, these visual tools have remained largely inaccessible to screen-reader users. We introduce AltGeoViz, a new interactive geovisualization approach that dynamically generates alt-text descriptions based on the user’s current map view, providing voiceover summaries of spatial patterns and descriptive statistics. In a remote user study with five screen-reader users, we found that participants were able to interact with spatial data in previously infeasible ways, demonstrated a clear understanding of data summaries and their location context, and could synthesize spatial understandings of their explorations. Moreover, we identified key areas for improvement, such as the addition of spatial navigation controls and comparative analysis features. Chu Li 0001, Rock Yuren Pang, Ather Sharif, Arnavi Chheda-Kothary, Jeffrey Heer, Jon Froehlich |
IEEE VIS | 4 |
| 2023 | Understanding Blind and Low Vision Users' Attitudes Towards Spatial Interactions in Desktop Screen ReadersabstractDesktop screen readers as a web navigation mechanism for BLV users are tedious and frozen in time, especially in the face of richer ways of presenting spatial information such as tactile and touchscreen devices. In our work, we consider what it means to create and evaluate systems that can present a similarly rich, spatial interaction mechanism plugged into existing screen reader paradigms. We present a formative study conducted with SpaceNav, a custom screen reader that utilizes spatial input and output to navigate two different web applications. We present results from this study, and discuss a new browser extension we are implementing based on our formative study feedback to more robustly test spatial interactions in the context of real world websites. To close, we describe our goals for evaluating the new web extension in a future study. Arnavi Chheda-Kothary, David A. Rios, Kynnedy Simone Smith, Avery Reyna, Cecilia Zhang, Brian A. Smith 0001 |
ASSETS | 1 |