Michelle Li

schoolMetabar_chart#3343 Global Rank

Total Papers

HCI Rank

#3343

Research Areas

Years Active

Recent Publications

Authoring LLM-Based Assistance for Real-World Contexts and Tasks

Advances in AI hold the possibility of assisting users with highly varied and individual needs, but the breadth of assistance that these systems could provide creates a challenge for how users specify their goals to the system. To support the authoring of AI assistance for real-world tasks, we propose the concept of C…

DIS '243rd AuthorAR Navigation & Context Awareness +2

Fidgets: Building Blocks for a Predictive UI Toolkit

The rapid growth of AR platforms, combined with the rising predictive power of intelligent systems, will fundamentally change interactive computing. Interaction will increasingly happen on the go, causing I/O to become constrained, ultimately leading to reliance on user intent prediction for aid. In this pictorial, we…

CHI '24CorrespondingHuman-LLM Collaboration +1

OmniActions: Predicting Digital Actions in Response to Real-World Multimodal Sensory Inputs with LLMs

The progression to "Pervasive Augmented Reality" envisions easy access to multimodal information continuously. However, in many everyday scenarios, users are occupied physically, cognitively or socially. This may increase the friction to act upon the multimodal information that users encounter in the world. To reduce…

CHI '235th+ AuthorHonorable MentionAR Navigation & Context Awareness +1

XAIR: A Framework of Explainable AI in Augmented Reality

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to d…

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