Yi-Chi Liao

schoolETH Zurichbar_chart#1229 Global Rank

Total Papers

HCI Rank

#1229

Research Areas

Years Active

Recent Publications

Automating UI Optimization through Multi-Agentic Reasoning

We present AutoOptimization, a novel multi-objective optimization framework for adapting user interfaces. From a user’s verbal preferences for changing a UI, our framework guides a prioritization-based Pareto frontier search over candidate layouts. It selects suitable objective functions for UI placement while simulta…

CHI '261st AuthorMid-Air Haptics (Ultrasonic) +3

Efficient Human-in-the-Loop Optimization via Priors Learned from User Models

Human-in-the-loop optimization identifies optimal interface designs by iteratively observing user performance. However, it often requires numerous iterations due to the lack of prior information. While recent approaches have accelerated this process by leveraging previous optimization data, collecting user data remain…

CHI '262nd AuthorGenerative AI (Text, Image, Music, Video) +2

Preference-Guided Prompt Optimization for Text-to-Image Generation

Generative models are increasingly powerful, yet users struggle to guide them through prompts. The generative process is difficult to control and unpredictable, and user instructions may be ambiguous or under-specified. Prior prompt refinement tools heavily rely on human effort, while prompt optimization methods focus…

UIST '253rd AuthorMixed Reality Workspaces +1

Preference-Guided Multi-Objective UI Adaptation

3D Mixed Reality interfaces have nearly unlimited space for layout placement, making automatic UI adaptation crucial for enhancing the user experience. Such adaptation is often formulated as a multi-objective optimization (MOO) problem, where multiple, potentially conflicting design objectives must be balanced. Howeve…

UIST '252nd AuthorExplainable AI (XAI) +1

Efficient Visual Appearance Optimization by Learning from Prior Preferences

Adjusting visual parameters such as brightness and contrast is common in our everyday experiences. Finding the optimal parameter setting is challenging due to the large search space and the lack of an explicit objective function, leaving users to rely solely on their implicit preferences. Prior work has explored Prefe…

IUI '251st AuthorExplainable AI (XAI) +2

Redefining Affordance via Computational Rationality

Affordances, a foundational concept in human-computer interaction and design, have traditionally been explained by direct-perception theories, which assume that individuals perceive action possibilities directly from the environment. However, these theories fall short of explaining how affordances are perceived, learn…

CHI '252nd AuthorTeleoperated Driving +1

3HANDS Dataset: Learning from Humans for Generating Naturalistic Handovers with Supernumerary Robotic Limbs

Supernumerary robotic limbs are robotic structures integrated closely with the user's body, which augment human physical capabilities and necessitate seamless, naturalistic human-machine interaction. For effective assistance in physical tasks, enabling SRLs to hand over objects to humans is crucial. Yet, designing heu…

CHI '251st AuthorHonorable MentionHuman-LLM Collaboration

Continual Human-in-the-Loop Optimization

Optimal input settings vary across users due to differences in motor abilities and personal preferences, which are typically addressed by manual tuning or calibration. Although human-in-the-loop optimization has the potential to identify optimal settings during use, it is rarely applied due to its long optimization pr…

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