Dr. Yifang Wang has received a Best Paper Honorable Mention at ACM CHI 2026 and has published multiple papers spanning human–AI collaboration, visual analytics, and AI-assisted healthcare.
CHI 2026 Honorable Mention Award
[CHI 2026] Interaction-Augmented Instruction: Modeling the Synergy of Prompts and Interactions in Human-GenAI Collaboration
This work has received a Best Paper Honorable Mention at CHI 2026, one of the premier conferences in human-computer interaction (HCI). The research addresses a fundamental limitation in current generative AI systems: while text prompts are widely used, they often fail to express fine-grained and referential user intent. The paper introduces the Interaction-Augmented Instruction (IAI) model, a compact entity–relation framework that formalizes how GUI-based interactions (e.g., brushing, clicking) and text prompts can be combined to enhance communication between humans and AI. Project Page: https://interaction-augmented-instruction.github.io/
Three Publications at TVCG and HEALTH
[TVCG 2026] DAVA: Decoding Art with Visual Analytics through Feature Modeling and Multi-Agent Collaboration
This work introduces a visual analytics system that combines feature modeling and multi-agent collaboration to support the interpretation of complex artworks. It demonstrates how AI and visualization can work together to enable deeper understanding in digital humanities and art analysis. Demo Video: https://doi.org/10.1109/TVCG.2026.3653892/mm1
[TVCG 2025] EMINDS: Understanding User Behavior Progression for Mental Health Exploration on Social Media
This research develops a visual analytics framework to model and explore user behavior progression related to mental health on social media platforms, enabling researchers to uncover patterns and trajectories in online behavioral data. Demo Video: https://doi.org/10.1109/TVCG.2025.3630646/mm3
[HEALTH 2026] DietGlance: Dietary Monitoring and Personalized Analysis at a Glance with Knowledge-Empowered AI Assistant
This work is published in ACM Transactions on Computing for Healthcare. The work presents an AI-powered augmented reality system for dietary monitoring and personalized nutrition analysis, leveraging knowledge-enhanced AI models to support everyday health decision-making.